{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "A9N7L47cJWYb",
      "metadata": {
        "id": "A9N7L47cJWYb"
      },
      "source": [
        "# **Script set-up**"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "uPJtNuVGJZp8",
      "metadata": {
        "id": "uPJtNuVGJZp8"
      },
      "source": [
        "## Import libraries and set global formatting parameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c67575db",
      "metadata": {
        "id": "c67575db",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e96ff72d-df85-437a-9ce2-2e300b79fb0b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting rasterio\n",
            "  Downloading rasterio-1.3.10-cp310-cp310-manylinux2014_x86_64.whl.metadata (14 kB)\n",
            "Collecting affine (from rasterio)\n",
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            "  Downloading snuggs-1.4.7-py3-none-any.whl.metadata (3.4 kB)\n",
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            "\u001b[?25hDownloading snuggs-1.4.7-py3-none-any.whl (5.4 kB)\n",
            "Downloading affine-2.4.0-py3-none-any.whl (15 kB)\n",
            "Installing collected packages: snuggs, affine, rasterio\n",
            "Successfully installed affine-2.4.0 rasterio-1.3.10 snuggs-1.4.7\n",
            "Collecting contextily\n",
            "  Downloading contextily-1.6.1-py3-none-any.whl.metadata (2.9 kB)\n",
            "Requirement already satisfied: geopy in /usr/local/lib/python3.10/dist-packages (from contextily) (2.4.1)\n",
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            "Collecting mercantile (from contextily)\n",
            "  Downloading mercantile-1.2.1-py3-none-any.whl.metadata (4.8 kB)\n",
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            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->contextily) (1.16.0)\n",
            "Downloading contextily-1.6.1-py3-none-any.whl (17 kB)\n",
            "Downloading mercantile-1.2.1-py3-none-any.whl (14 kB)\n",
            "Installing collected packages: mercantile, contextily\n",
            "Successfully installed contextily-1.6.1 mercantile-1.2.1\n"
          ]
        }
      ],
      "source": [
        "# import utility libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.ticker as mtick\n",
        "from google.colab import drive\n",
        "import os\n",
        "import seaborn as sns\n",
        "import matplotlib.cm as cm\n",
        "import matplotlib.colors as mcolors\n",
        "\n",
        "# import geospatial libraries\n",
        "import fiona as fio\n",
        "!pip install rasterio\n",
        "import rasterio as rio\n",
        "from rasterio.mask import mask\n",
        "from osgeo import gdal\n",
        "import geopandas as gpd\n",
        "from rasterio.features import shapes\n",
        "from shapely.geometry import shape\n",
        "from rasterio.warp import reproject, Resampling, calculate_default_transform\n",
        "from shapely.strtree import STRtree\n",
        "from shapely.geometry import Point\n",
        "from scipy.spatial import cKDTree\n",
        "from rasterio.features import rasterize\n",
        "from rasterio.features import geometry_mask\n",
        "from scipy.ndimage import distance_transform_edt\n",
        "!pip install contextily\n",
        "import contextily as ctx"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5arOi8vPJio1",
      "metadata": {
        "id": "5arOi8vPJio1"
      },
      "outputs": [],
      "source": [
        "# set pandas to format ints with ',' as thousands separator\n",
        "pd.options.display.float_format = '{:,}'.format"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "Qw_OOMosJdFj",
      "metadata": {
        "id": "Qw_OOMosJdFj"
      },
      "source": [
        "# **Data preparation**"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "de_oZPciJf1R",
      "metadata": {
        "id": "de_oZPciJf1R"
      },
      "source": [
        "## Define universal variables"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fda57f62",
      "metadata": {
        "id": "fda57f62"
      },
      "outputs": [],
      "source": [
        "countries = ['Angola',\n",
        "             'Benin',\n",
        "             'Botswana',\n",
        "             'Burkina Faso',\n",
        "             'Burundi',\n",
        "             'Cameroon',\n",
        "             'Central African Republic',\n",
        "             'Chad',\n",
        "             'DRC',\n",
        "             'ROC',\n",
        "             'CDI',\n",
        "             'Equatorial Guinea',\n",
        "             'Eritrea',\n",
        "             'Swaziland',\n",
        "             'Ethiopia',\n",
        "             'Gabon',\n",
        "             'Gambia',\n",
        "             'Ghana',\n",
        "             'Guinea',\n",
        "             'Guinea-Bissau',\n",
        "             'Kenya',\n",
        "             'Lesotho',\n",
        "             'Liberia',\n",
        "             'Madagascar',\n",
        "             'Malawi',\n",
        "             'Mali',\n",
        "             'Mauritania',\n",
        "             'Mozambique',\n",
        "             'Namibia',\n",
        "             'Niger',\n",
        "             'Nigeria',\n",
        "             'Rwanda',\n",
        "             'Senegal',\n",
        "             'Sierra Leone',\n",
        "             'Somalia',\n",
        "             'South Africa',\n",
        "             'South Sudan',\n",
        "             'Sudan',\n",
        "             'Tanzania',\n",
        "             'Togo',\n",
        "             'Uganda',\n",
        "             'Zambia',\n",
        "             'Zimbabwe'\n",
        "             ]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "72XtMRRtJ2he",
      "metadata": {
        "id": "72XtMRRtJ2he"
      },
      "source": [
        "## Define file paths"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "r2eR1QmVJ3JP",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r2eR1QmVJ3JP",
        "outputId": "e7720adc-ad3e-443e-b51a-e25ed80abee1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "drive.mount('/content/drive')\n",
        "path = ('/content/drive/My Drive/Rurban/')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "GTs1-WSCKYK_",
      "metadata": {
        "id": "GTs1-WSCKYK_"
      },
      "outputs": [],
      "source": [
        "fp = path"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "F4TN8NzEJnaG",
      "metadata": {
        "id": "F4TN8NzEJnaG"
      },
      "source": [
        "## Clip rasters to country boundaries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9d5f0a09",
      "metadata": {
        "id": "9d5f0a09"
      },
      "outputs": [],
      "source": [
        "for cntry in countries:\n",
        "    adm_bndry_fn = f'Administrative Boundaries/{cntry} Admin Boundaries.shp'\n",
        "\n",
        "    # read in country admin boundaries\n",
        "    with fio.open(fp+adm_bndry_fn, 'r') as src:\n",
        "        adm_bndry_geom = [src[0]['geometry']]\n",
        "\n",
        "    # read in and mask pop, electrification, and urca raster data\n",
        "    with rio.open(fp+'AtlasAI Data/Electrification_0_7_Africa_2015_v2.tif') as src:\n",
        "        elec, elec_trnsfrm = mask(src, adm_bndry_geom, crop=True)\n",
        "        elec_meta = src.meta.copy()\n",
        "\n",
        "    with rio.open(fp+'AtlasAI Data/Population_4_1_Africa_2015.tif') as src:\n",
        "        pop, pop_trnsfrm = mask(src, adm_bndry_geom, crop=True)\n",
        "        pop_meta = src.meta.copy()\n",
        "\n",
        "    with rio.open(fp+'URCA Raw.tif') as src:\n",
        "        urca, urca_trnsfrm = mask(src, adm_bndry_geom, crop=True)\n",
        "        urca_meta = src.meta.copy()\n",
        "\n",
        "    # update metadata for masked rasters\n",
        "    elec_meta.update({'height': elec.shape[1], 'width': elec.shape[2], 'transform': elec_trnsfrm})\n",
        "    pop_meta.update({'height': pop.shape[1], 'width': pop.shape[2], 'transform': pop_trnsfrm})\n",
        "    urca_meta.update({'height': urca.shape[1], 'width': urca.shape[2], 'transform': urca_trnsfrm})\n",
        "\n",
        "    # mask pop < 1\n",
        "    pop[pop<1] = 0\n",
        "\n",
        "    # write masked pop, electrification, and urca raster data to drive\n",
        "    with rio.open(f'{fp}Clipped Data/{cntry}_Masked_Pop.tif', 'w', **pop_meta) as dest:\n",
        "        dest.write(pop)\n",
        "\n",
        "    with rio.open(f'{fp}Clipped Data/{cntry}_Masked_Elec.tif', 'w', **elec_meta) as dest:\n",
        "        dest.write(elec)\n",
        "\n",
        "    with rio.open(f'{fp}Clipped Data/{cntry}_Masked_URCA.tif', 'w', **urca_meta) as dest:\n",
        "        dest.write(urca)\n",
        "\n",
        "    # resample urca resolution and extent to match pop and electrification\n",
        "    masked_pop = gdal.Open(f'{fp}Clipped Data/{cntry}_Masked_Pop.tif', gdal.GA_ReadOnly)\n",
        "\n",
        "    x_res = masked_pop.GetGeoTransform()[1]\n",
        "    y_res = -masked_pop.GetGeoTransform()[5]\n",
        "\n",
        "    xmin = min(masked_pop.GetGeoTransform()[0],\n",
        "               masked_pop.GetGeoTransform()[0] + masked_pop.RasterXSize * masked_pop.GetGeoTransform()[1])\n",
        "    ymin = min(masked_pop.GetGeoTransform()[3],\n",
        "               masked_pop.GetGeoTransform()[3] + masked_pop.RasterYSize * masked_pop.GetGeoTransform()[5])\n",
        "    xmax = max(masked_pop.GetGeoTransform()[0],\n",
        "               masked_pop.GetGeoTransform()[0] + masked_pop.RasterXSize * masked_pop.GetGeoTransform()[1])\n",
        "    ymax = max(masked_pop.GetGeoTransform()[3],\n",
        "               masked_pop.GetGeoTransform()[3] + masked_pop.RasterYSize * masked_pop.GetGeoTransform()[5])\n",
        "\n",
        "    gdal_params = {'format': 'GTiff', 'xRes': x_res, 'yRes': y_res, 'outputBounds': [xmin, ymin, xmax, ymax]}\n",
        "    ds = gdal.Warp(f'{fp}Clipped Data/{cntry}_Resampled_Masked_URCA.tif',\n",
        "                   f'{fp}Clipped Data/{cntry}_Masked_URCA.tif',\n",
        "                   **gdal_params)\n",
        "\n",
        "del ds #<--close final resampled urca raster"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8ug-M6FlKnaj",
      "metadata": {
        "id": "8ug-M6FlKnaj"
      },
      "source": [
        "# **Analysis**"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0837a6ee",
      "metadata": {
        "id": "0837a6ee"
      },
      "source": [
        "## Calculate total, electrified, and unelectrified population and electrification rate per country"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "01efa0e4",
      "metadata": {
        "id": "01efa0e4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5cf188d9-94de-4efc-d934-2f5b96294e86"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-73-8cd4b7b4312b>:20: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
            "  cntry_pop_df = pd.concat([cntry_pop_df, new_row], ignore_index=True)\n"
          ]
        }
      ],
      "source": [
        "# Initialize the DataFrame\n",
        "cntry_pop_df = pd.DataFrame(columns=['Country', 'Total_Pop', 'Elec_Pop', 'Unelec_Pop', 'Elec_Rate'])\n",
        "\n",
        "# Loop through each country and calculate the required values\n",
        "for cntry in countries:\n",
        "    with rio.open(f'{fp}Clipped Data/{cntry}_Masked_Pop.tif', 'r') as src:\n",
        "        pop = src.read(1)\n",
        "\n",
        "    with rio.open(f'{fp}Clipped Data/{cntry}_Masked_Elec.tif', 'r') as src:\n",
        "        elec = src.read(1)\n",
        "\n",
        "    total_pop = round(pop.sum())\n",
        "    elec_pop = round(np.array(pop * elec).sum())\n",
        "    unelec_pop = round(total_pop - elec_pop)\n",
        "    elec_rate = round(elec_pop / total_pop, 4)\n",
        "\n",
        "    new_row = pd.DataFrame([[cntry, total_pop, elec_pop, unelec_pop, elec_rate]],\n",
        "                           columns=cntry_pop_df.columns)\n",
        "\n",
        "    cntry_pop_df = pd.concat([cntry_pop_df, new_row], ignore_index=True)\n",
        "\n",
        "# Convert columns to appropriate data types\n",
        "cntry_pop_df = cntry_pop_df.astype({col: int if col != 'Elec_Rate' else float for col in cntry_pop_df.columns[1:]})\n",
        "\n",
        "# Ensure the output directory exists\n",
        "output_dir = os.path.join(fp, 'Outputs')\n",
        "os.makedirs(output_dir, exist_ok=True)\n",
        "\n",
        "# Save DataFrame to CSV file\n",
        "output_path = os.path.join(output_dir, 'elec_unelec_output.csv')\n",
        "cntry_pop_df.to_csv(output_path, index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4684488c",
      "metadata": {
        "id": "4684488c"
      },
      "source": [
        "## Calculate electrified and unelectrified population per rural/urban binary per country"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b3e28deb",
      "metadata": {
        "id": "b3e28deb"
      },
      "outputs": [],
      "source": [
        "# Initialize DataFrame\n",
        "cntry_urbn_rurl_elec_unelec_df = pd.DataFrame(\n",
        "    columns=['Country', 'Total_Pop', 'Elec_Pop', 'Unelec_Pop',\n",
        "             'Urban_Total_Pop', 'Urban_Elec_Pop', 'Urban_Unelec_Pop',\n",
        "             'Rural_Total_Pop', 'Rural_Elec_Pop', 'Rural_Unelec_Pop']\n",
        ")\n",
        "\n",
        "# Process each country\n",
        "for cntry in countries:\n",
        "    with rio.open(f'{fp}Clipped Data/{cntry}_Masked_Pop.tif', 'r') as src:\n",
        "        pop = src.read(1)\n",
        "\n",
        "    with rio.open(f'{fp}Clipped Data/{cntry}_Masked_Elec.tif', 'r') as src:\n",
        "        elec = src.read(1)\n",
        "\n",
        "    with rio.open(f'{fp}Clipped Data/{cntry}_Africapolis_Rasterized.tif', 'r') as src:\n",
        "        urbn = src.read(1)\n",
        "\n",
        "    # Uncomment below to use Khavari instead of Africapolis\n",
        "    # with rio.open(f'{fp}Clipped Data/{cntry}_Khavari_Rasterized.tif', 'r') as src:\n",
        "    #     urbn = src.read(1)\n",
        "    # urbn = np.where(urbn == 2, 1, 0)  # Convert Khavari urban value (2) to 1 and peri-urban value (1) to 0\n",
        "\n",
        "    elec[elec == 255] = 0  # Convert no-data value (255) to 0\n",
        "\n",
        "    total_pop = round(pop.sum())\n",
        "    elec_pop = round(np.array(pop * elec).sum())\n",
        "    unelec_pop = round(np.array(pop * (-elec + 1)).sum())\n",
        "\n",
        "    total_urbn_pop = round(np.array(pop * urbn).sum())\n",
        "    elec_urbn_pop = np.array(pop * elec * urbn).sum()\n",
        "    unelec_urbn_pop = np.array(pop * (-elec + 1) * urbn).sum()\n",
        "\n",
        "    total_rurl_pop = round(np.array(pop * (-urbn + 1)).sum())  # Rural = -urbn + 1\n",
        "    elec_rurl_pop = np.array(pop * elec * (-urbn + 1)).sum()\n",
        "    unelec_rurl_pop = np.array(pop * (-elec + 1) * (-urbn + 1)).sum()\n",
        "\n",
        "    new_row = pd.DataFrame(\n",
        "        [[cntry, total_pop, elec_pop, unelec_pop,\n",
        "          total_urbn_pop, elec_urbn_pop, unelec_urbn_pop,\n",
        "          total_rurl_pop, elec_rurl_pop, unelec_rurl_pop]],\n",
        "        columns=cntry_urbn_rurl_elec_unelec_df.columns\n",
        "    )\n",
        "\n",
        "    cntry_urbn_rurl_elec_unelec_df = pd.concat([cntry_urbn_rurl_elec_unelec_df, new_row], ignore_index=True)\n",
        "\n",
        "# Convert values back to integers\n",
        "cntry_urbn_rurl_elec_unelec_df = cntry_urbn_rurl_elec_unelec_df.astype(\n",
        "    {col: int for col in cntry_urbn_rurl_elec_unelec_df.columns[1:]}\n",
        ")\n",
        "\n",
        "# Ensure the output directory exists\n",
        "output_dir = os.path.join(fp, 'Outputs')\n",
        "os.makedirs(output_dir, exist_ok=True)\n",
        "\n",
        "# Save DataFrame to CSV file\n",
        "output_path = os.path.join(output_dir, 'rural_urban_elec_unelec_output.csv')\n",
        "cntry_urbn_rurl_elec_unelec_df.to_csv(output_path, index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "68a95731",
      "metadata": {
        "id": "68a95731"
      },
      "outputs": [],
      "source": [
        "# Define file paths\n",
        "africapolis_shpfn = 'Urban Extent Data/AFRICAPOLIS2020.shp'\n",
        "\n",
        "# Process each country\n",
        "for cntry in countries:\n",
        "    africapolis_fn = f'Clipped Data/{cntry}_Africapolis_Rasterized.tif'\n",
        "\n",
        "    elec_ds = gdal.Open(f'{fp}Clipped Data/{cntry}_Masked_Elec.tif')\n",
        "    africapolis_ds = gdal.OpenEx(fp + africapolis_shpfn)\n",
        "\n",
        "    geot = elec_ds.GetGeoTransform()\n",
        "\n",
        "    drv_tiff = gdal.GetDriverByName(\"GTiff\")\n",
        "    chn_africapolis_ds = drv_tiff.Create(fp + africapolis_fn, elec_ds.RasterXSize,\n",
        "                                         elec_ds.RasterYSize, 1, gdal.GDT_Float32)\n",
        "\n",
        "    chn_africapolis_ds.SetGeoTransform(geot)\n",
        "\n",
        "    gdal.Rasterize(chn_africapolis_ds, africapolis_ds, burnValues=[1], allTouched=True)\n",
        "    chn_africapolis_ds.GetRasterBand(1).SetNoDataValue(0.0)\n",
        "\n",
        "    # Close datasets\n",
        "    chn_africapolis_ds = None\n",
        "    elec_ds = None\n",
        "    africapolis_ds = None\n",
        "\n",
        "# Uncomment below to use Khavari instead of Africapolis\n",
        "# for cntry in countries:\n",
        "#     khavari_shpfn = f'Urban Extent Data/Khavari/{cntry}.shp'\n",
        "#     khavari_fn = f'Clipped Data/{cntry}_Khavari_Rasterized.tif'\n",
        "\n",
        "#     elec_ds = gdal.Open(f'{fp}Clipped Data/{cntry}_Masked_Elec.tif')\n",
        "#     khavari_ds = gdal.OpenEx(fp + khavari_shpfn)\n",
        "\n",
        "#     geot = elec_ds.GetGeoTransform()\n",
        "\n",
        "#     drv_tiff = gdal.GetDriverByName(\"GTiff\")\n",
        "#     chn_khavari_ds = drv_tiff.Create(fp + khavari_fn, elec_ds.RasterXSize,\n",
        "#                                      elec_ds.RasterYSize, 1, gdal.GDT_Float32)\n",
        "\n",
        "#     chn_khavari_ds.SetGeoTransform(geot)\n",
        "\n",
        "#     gdal.Rasterize(chn_khavari_ds, khavari_ds, attribute='IsUrban', allTouched=True)\n",
        "#     chn_khavari_ds.GetRasterBand(1).SetNoDataValue(0.0)\n",
        "\n",
        "#     # Close datasets\n",
        "#     chn_khavari_ds = None\n",
        "#     elec_ds = None\n",
        "#     khavari_ds = None"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1a32df54",
      "metadata": {
        "id": "1a32df54"
      },
      "source": [
        "## Calculate electrified and unelectrified population per rural/urban binary per URCA category per country"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "36048ea2",
      "metadata": {
        "id": "36048ea2"
      },
      "outputs": [],
      "source": [
        "# Initialize DataFrame with required columns\n",
        "rural_urban_elec_unelec_URCA_output = pd.DataFrame(columns=['Country'] + [\n",
        "    f'{cat}_{headr}' for cat in range(1, 31) for headr in [\n",
        "        'Pop', 'Elec_Pop', 'Unelec_Pop',\n",
        "        'Urban_Pop', 'Rural_Pop',\n",
        "        'Urban_Elec_Pop', 'Rural_Elec_Pop',\n",
        "        'Urban_Unelec_Pop', 'Rural_Unelec_Pop'\n",
        "    ]\n",
        "])\n",
        "\n",
        "# Process each country\n",
        "for cntry in countries:\n",
        "    adm_bndry_fn = f'Administrative Boundaries/{cntry} Admin Boundaries.shp'\n",
        "\n",
        "    # Read in country admin boundaries\n",
        "    with fio.open(fp + adm_bndry_fn, 'r') as src:\n",
        "        adm_bndry_geom = [src[0]['geometry']]\n",
        "\n",
        "    # Read in pop, electrification, URCA, and Africapolis raster data\n",
        "    with rio.open(f'{fp}Clipped Data/{cntry}_Masked_Pop.tif', 'r') as src:\n",
        "        pop = src.read(1)\n",
        "\n",
        "    with rio.open(f'{fp}Clipped Data/{cntry}_Masked_Elec.tif', 'r') as src:\n",
        "        elec = src.read(1)\n",
        "\n",
        "    with rio.open(f'{fp}Clipped Data/{cntry}_Resampled_Masked_URCA.tif', 'r') as src:\n",
        "        urca = src.read(1)\n",
        "\n",
        "    with rio.open(f'{fp}Clipped Data/{cntry}_Africapolis_Rasterized.tif', 'r') as src:\n",
        "        urbn = src.read(1)\n",
        "\n",
        "    \"\"\"uncomment below to use Khavari instead of Africapolis\"\"\"\n",
        "    # with rio.open(f'{fp}Clipped Data/{cntry}_Khavari_Rasterized.tif', 'r') as src:\n",
        "    #     urbn = src.read(1)\n",
        "    # urbn = np.where(urbn == 2, 1, 0)  # Convert Khavari urban value (2) to 1 and peri-urban value (1) to 0\n",
        "\n",
        "    # Prepare the new row for the DataFrame\n",
        "    new_row = pd.DataFrame([[cntry] + [\n",
        "        val for vals in [[\n",
        "            round(np.array(np.where(urca == cat, 1, 0) * pop).sum()),\n",
        "            round(np.array(np.where(urca == cat, 1, 0) * pop * elec).sum()),\n",
        "            round(np.array(np.where(urca == cat, 1, 0) * pop * (-elec + 1)).sum()),\n",
        "            round(np.array(np.where(urca == cat, 1, 0) * pop * urbn).sum()),\n",
        "            round(np.array(np.where(urca == cat, 1, 0) * pop * (-urbn + 1)).sum()),\n",
        "            round(np.array(np.where(urca == cat, 1, 0) * pop * elec * urbn).sum()),\n",
        "            round(np.array(np.where(urca == cat, 1, 0) * pop * elec * (-urbn + 1)).sum()),\n",
        "            round(np.array(np.where(urca == cat, 1, 0) * pop * (-elec + 1) * urbn).sum()),\n",
        "            round(np.array(np.where(urca == cat, 1, 0) * pop * (-elec + 1) * (-urbn + 1)).sum())\n",
        "        ] for cat in range(1, 31)] for val in vals\n",
        "    ]], columns=rural_urban_elec_unelec_URCA_output.columns)\n",
        "\n",
        "    # Append the new row to the DataFrame\n",
        "    rural_urban_elec_unelec_URCA_output = pd.concat([rural_urban_elec_unelec_URCA_output, new_row], ignore_index=True)\n",
        "\n",
        "# Convert population values back to integers\n",
        "rural_urban_elec_unelec_URCA_output = rural_urban_elec_unelec_URCA_output.astype({col: int for col in rural_urban_elec_unelec_URCA_output.columns[1:]})\n",
        "\n",
        "# Ensure the output directory exists\n",
        "output_dir = os.path.join(fp, 'Outputs')\n",
        "os.makedirs(output_dir, exist_ok=True)\n",
        "\n",
        "# Save DataFrame to CSV file\n",
        "output_path = os.path.join(output_dir, 'rural_urban_elec_unelec_URCA_output.csv')\n",
        "rural_urban_elec_unelec_URCA_output.to_csv(output_path, index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "V7FItxwPXmos",
      "metadata": {
        "id": "V7FItxwPXmos"
      },
      "source": [
        "## Calculate population density of unelectrified by URCA category"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "n2qobXaGlCLj",
      "metadata": {
        "id": "n2qobXaGlCLj"
      },
      "outputs": [],
      "source": [
        "# Define the function to calculate the area of each cell in square kilometers\n",
        "def calculate_cell_area(transform, height, width):\n",
        "    cell_size = transform[0] / 1000  # pixel size in km (assuming square pixels)\n",
        "    cell_area = cell_size ** 2  # Cell area in square kilometers\n",
        "    cell_area_array = np.full((height, width), cell_area, dtype=np.float32)\n",
        "    return cell_area_array\n",
        "\n",
        "# Define the function to calculate average density of unelectrified population by URCA category\n",
        "def calculate_average_density(input_df, country, urca_data, cell_area):\n",
        "    total_unelec_pop = np.zeros(30)\n",
        "    total_area = np.zeros(30)\n",
        "\n",
        "    for category in range(1, 31):\n",
        "        unelectrified_pop = input_df.loc[input_df['Country'] == country, f'{category}_Unelec_Pop'].values[0]\n",
        "\n",
        "        category_area = cell_area[urca_data == category].sum()\n",
        "\n",
        "        total_unelec_pop[category - 1] = unelectrified_pop\n",
        "        total_area[category - 1] = category_area\n",
        "\n",
        "    average_density = np.divide(total_unelec_pop, total_area, out=np.zeros_like(total_unelec_pop, dtype=float), where=total_area != 0)\n",
        "    return average_density\n",
        "\n",
        "# Define a function to resample rasters to a common resolution and shape\n",
        "def resample_raster(src_raster, ref_raster):\n",
        "    with rio.open(ref_raster) as ref:\n",
        "        ref_transform = ref.transform\n",
        "        ref_width = ref.width\n",
        "        ref_height = ref.height\n",
        "        ref_crs = ref.crs\n",
        "        ref_profile = ref.profile\n",
        "\n",
        "    with rio.open(src_raster) as src:\n",
        "        src_data = src.read(1)\n",
        "        src_transform = src.transform\n",
        "        src_crs = src.crs\n",
        "\n",
        "        # Create a destination array with the same shape as the reference raster\n",
        "        dest_data = np.empty((ref_height, ref_width), dtype=np.float32)\n",
        "\n",
        "        # Reproject the source raster to the destination array\n",
        "        reproject(\n",
        "            source=src_data,\n",
        "            destination=dest_data,\n",
        "            src_transform=src_transform,\n",
        "            src_crs=src_crs,\n",
        "            dst_transform=ref_transform,\n",
        "            dst_crs=ref_crs,\n",
        "            resampling=Resampling.nearest\n",
        "        )\n",
        "\n",
        "    return dest_data, ref_transform, ref_profile\n",
        "\n",
        "# Process the data for all countries and create a combined raster for average density\n",
        "categories = range(1, 31)\n",
        "\n",
        "# Use the first country's raster as the reference for common resolution and shape\n",
        "ref_raster_path = f'{fp}Clipped Data/{countries[0]}_Masked_Pop.tif'\n",
        "\n",
        "# Initialize arrays to hold the combined unelectrified population and area for each category\n",
        "combined_unelec_pop = np.zeros(30)\n",
        "combined_area = np.zeros(30)\n",
        "\n",
        "# Loop through each country\n",
        "for country in countries:\n",
        "    # Resample the URCA data to the common resolution and shape\n",
        "    urca_raster_path = f'{fp}Clipped Data/{country}_Resampled_Masked_URCA.tif'\n",
        "    urca_data, ref_transform, ref_profile = resample_raster(urca_raster_path, ref_raster_path)\n",
        "\n",
        "    # Calculate the area of each cell in square kilometers\n",
        "    height, width = urca_data.shape\n",
        "    cell_area = calculate_cell_area(ref_transform, height, width)\n",
        "\n",
        "    # Calculate the average density for the current country\n",
        "    average_density = calculate_average_density(output_df, country, urca_data, cell_area)\n",
        "\n",
        "    # Update the combined population and area for each category\n",
        "    for category in range(1, 31):\n",
        "        combined_unelec_pop[category - 1] += output_df.loc[output_df['Country'] == country, f'{category}_Unelec_Pop'].values[0]\n",
        "        combined_area[category - 1] += cell_area[urca_data == category].sum()\n",
        "\n",
        "# Calculate the combined average density\n",
        "combined_average_density = np.divide(combined_unelec_pop, combined_area, out=np.zeros_like(combined_unelec_pop, dtype=float), where=combined_area != 0)\n",
        "\n",
        "# Initialize an array to hold the combined average density data\n",
        "combined_avg_density_data = np.zeros((height, width), dtype=np.float32)\n",
        "\n",
        "# Loop through each category and update the combined average density data\n",
        "for category in categories:\n",
        "    combined_avg_density_data = np.where(urca_data == category, combined_average_density[category - 1], combined_avg_density_data)\n",
        "\n",
        "# Mask the combined density data to include only non-zero areas\n",
        "combined_avg_density_data = np.where(combined_avg_density_data > 0, combined_avg_density_data, np.nan)\n",
        "\n",
        "# Define the output file path\n",
        "output_file = f'{fp}Graphics/Geospatial Density/Combined_Average_Unelec_Density.tif'\n",
        "\n",
        "# Remove the existing file if it exists\n",
        "if os.path.exists(output_file):\n",
        "    os.remove(output_file)\n",
        "\n",
        "# Update profile for the output raster\n",
        "ref_profile.update(dtype=rio.float32, count=1, compress='lzw', nodata=0)\n",
        "\n",
        "# Write the combined average density data to a single raster\n",
        "with rio.open(output_file, 'w', **ref_profile) as dst:\n",
        "    dst.write(combined_avg_density_data, 1)\n",
        "\n",
        "    # Update the metadata with the appropriate transform\n",
        "    dst.update_tags(transform=ref_transform)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "Uv-Ud6UJDOCH",
      "metadata": {
        "id": "Uv-Ud6UJDOCH"
      },
      "source": [
        "## Distribution of unelectrified by physical distance to nearest electrified cell"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Ensure the Dists directory exists\n",
        "dists_path = os.path.join(path, 'Dists')\n",
        "os.makedirs(dists_path, exist_ok=True)\n",
        "\n",
        "for cntry in countries:\n",
        "    print(f'Processing {cntry}')\n",
        "\n",
        "    adm_bndry_fn = f'Administrative Boundaries/{cntry} Admin Boundaries.shp'\n",
        "\n",
        "    # Read in country admin boundaries\n",
        "    with fio.open(path + adm_bndry_fn, 'r') as src:\n",
        "        adm_bndry_geom = [src[0]['geometry']]\n",
        "\n",
        "    # Read in and use preprocessed clipped raster data\n",
        "    elec_path = f'{path}Clipped Data/{cntry}_Masked_Elec.tif'\n",
        "    urca_path = f'{path}Clipped Data/{cntry}_Resampled_Masked_URCA.tif'\n",
        "    pop_path = f'{path}Clipped Data/{cntry}_Masked_Pop.tif'\n",
        "\n",
        "    with rio.open(elec_path) as src:\n",
        "        elec = src.read(1)\n",
        "        elec_transform = src.transform\n",
        "        elec_meta = src.meta.copy()\n",
        "\n",
        "    with rio.open(urca_path) as src:\n",
        "        urca = src.read(1)\n",
        "        urca_transform = src.transform\n",
        "        urca_meta = src.meta.copy()\n",
        "\n",
        "    with rio.open(pop_path) as src:\n",
        "        pop = src.read(1)\n",
        "        pop_transform = src.transform\n",
        "        pop_meta = src.meta.copy()\n",
        "\n",
        "    # Create a mask for all electrified cells (irrespective of URCA category)\n",
        "    all_electrified_mask = (elec > 0)\n",
        "\n",
        "    # Initialize dictionaries to store distances and populations for each URCA category\n",
        "    distances_per_urca = {}\n",
        "    populations_per_urca = {}\n",
        "\n",
        "    # Calculate the distance and population for each URCA category\n",
        "    for urca_cat in range(1, 31):\n",
        "        # Create a mask for the current URCA category\n",
        "        urca_mask = (urca == urca_cat)\n",
        "\n",
        "        # Electrified and unelectrified masks within the current URCA category\n",
        "        unelectrified_mask = (elec == 0) & urca_mask\n",
        "\n",
        "        # Extract the coordinates of the centroids of unelectrified and electrified cells\n",
        "        unelectrified_indices = np.argwhere(unelectrified_mask)\n",
        "        electrified_indices = np.argwhere(all_electrified_mask)\n",
        "\n",
        "        # Debugging: Print the number of unelectrified and electrified cells\n",
        "        print(f\"URCA {urca_cat} - Unelectrified cells: {len(unelectrified_indices)}, Electrified cells: {len(electrified_indices)}\")\n",
        "\n",
        "        # Convert indices to coordinates using high precision\n",
        "        unelectrified_coords = [Point(*rio.transform.xy(elec_transform, row, col, offset='center')) for row, col in unelectrified_indices]\n",
        "        electrified_coords = [Point(*rio.transform.xy(elec_transform, row, col, offset='center')) for row, col in electrified_indices]\n",
        "\n",
        "        # Debugging: Print some sample coordinates\n",
        "        if unelectrified_coords:\n",
        "            print(f\"URCA {urca_cat} - Sample unelectrified coordinates: {unelectrified_coords[:5]}\")\n",
        "        if electrified_coords:\n",
        "            print(f\"URCA {urca_cat} - Sample electrified coordinates: {electrified_coords[:5]}\")\n",
        "\n",
        "        if unelectrified_coords and electrified_coords:\n",
        "            unelectrified_gdf = gpd.GeoDataFrame(geometry=unelectrified_coords)\n",
        "            electrified_gdf = gpd.GeoDataFrame(geometry=electrified_coords)\n",
        "\n",
        "            # Build a KDTree for efficient nearest neighbor search\n",
        "            tree = cKDTree(np.array([(point.x, point.y) for point in electrified_coords]))\n",
        "\n",
        "            # Calculate distances from each unelectrified cell to the nearest electrified cell\n",
        "            distances, _ = tree.query(np.array([(point.x, point.y) for point in unelectrified_coords]))\n",
        "\n",
        "            # Debugging: Print some sample distances before conversion\n",
        "            print(f\"URCA {urca_cat} - Sample distances (in meters): {distances[:5]}\")\n",
        "\n",
        "            # Convert distances to kilometers\n",
        "            distances_km = distances / 1000  # Assuming the coordinates are in meters\n",
        "\n",
        "            # Debugging: Print some sample distances after conversion\n",
        "            print(f\"URCA {urca_cat} - Sample distances (in km): {distances_km[:5]}\")\n",
        "\n",
        "            # Store the distances and populations in the dictionaries\n",
        "            distances_per_urca[urca_cat] = distances_km.tolist()\n",
        "            populations_per_urca[urca_cat] = [pop[row, col] for row, col in unelectrified_indices]\n",
        "        else:\n",
        "            distances_per_urca[urca_cat] = []\n",
        "            populations_per_urca[urca_cat] = []\n",
        "\n",
        "        print(f\"URCA {urca_cat} - Average Distance: {np.mean(distances_per_urca[urca_cat]) if distances_per_urca[urca_cat] else 'No data'}\")\n",
        "\n",
        "    # Convert the dictionaries to DataFrames\n",
        "    df_distances = pd.DataFrame.from_dict(distances_per_urca, orient='index').transpose()\n",
        "    df_populations = pd.DataFrame.from_dict(populations_per_urca, orient='index').transpose()\n",
        "\n",
        "    # Save the DataFrames to CSV files\n",
        "    df_distances.to_csv(f'{dists_path}/{cntry}_Dists.csv', index=False)\n",
        "    df_populations.to_csv(f'{dists_path}/{cntry}_Populations.csv', index=False)"
      ],
      "metadata": {
        "id": "JoBy4R92WEwj"
      },
      "id": "JoBy4R92WEwj",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Path to the directory containing distance and population data\n",
        "dists_path = os.path.join(path, 'Dists')\n",
        "os.makedirs(dists_path, exist_ok=True)\n",
        "\n",
        "# Create an empty list to store the results\n",
        "results = []\n",
        "\n",
        "# Initialize variables for the final weighted value across all countries\n",
        "total_weighted_distance_all_countries = 0\n",
        "total_population_all_countries = 0\n",
        "\n",
        "# Loop through each country and calculate the weighted distance\n",
        "for cntry in countries:\n",
        "    # Load the distance and population data\n",
        "    df_distances = pd.read_csv(os.path.join(dists_path, f'{cntry}_Dists.csv'))\n",
        "    df_populations = pd.read_csv(os.path.join(dists_path, f'{cntry}_Populations.csv'))\n",
        "\n",
        "    # Initialize a dictionary to store the weighted distances\n",
        "    weighted_distances = {'Country': cntry}\n",
        "\n",
        "    # Calculate the weighted distance for each URCA category\n",
        "    total_weighted_distance = 0\n",
        "    category_count = 0\n",
        "\n",
        "    for category in df_distances.columns:\n",
        "        if category != 'Country':  # Ensure 'Country' column is not included\n",
        "            total_population = df_populations[category].sum()\n",
        "            if total_population > 0:\n",
        "                weighted_distance = (df_distances[category] * df_populations[category]).sum() / total_population\n",
        "            else:\n",
        "                weighted_distance = 0\n",
        "            weighted_distances[category] = weighted_distance\n",
        "\n",
        "            # Add to the total weighted distance and increase the category count\n",
        "            total_weighted_distance += weighted_distance\n",
        "            category_count += 1\n",
        "\n",
        "            # Add to the global total weighted distance and population\n",
        "            total_weighted_distance_all_countries += (df_distances[category] * df_populations[category]).sum()\n",
        "            total_population_all_countries += total_population\n",
        "\n",
        "    # Calculate the average weighted distance across all URCA categories\n",
        "    if category_count > 0:\n",
        "        average_weighted_distance = total_weighted_distance / category_count\n",
        "    else:\n",
        "        average_weighted_distance = 0\n",
        "\n",
        "    # Add the average weighted distance to the dictionary\n",
        "    weighted_distances['Average_Weighted_Distance'] = average_weighted_distance\n",
        "\n",
        "    # Append the results to the list\n",
        "    results.append(weighted_distances)\n",
        "\n",
        "# Create the output DataFrame from the results list\n",
        "dist_output_df = pd.DataFrame(results)\n",
        "\n",
        "# Save the output DataFrame as a CSV file\n",
        "output_path = os.path.join(dists_path, 'dist_output_df.csv')\n",
        "dist_output_df.to_csv(output_path, index=False)\n",
        "\n",
        "# Calculate the final weighted value across all countries and categories\n",
        "if total_population_all_countries > 0:\n",
        "    total_dist_avg = total_weighted_distance_all_countries / total_population_all_countries\n",
        "else:\n",
        "    total_dist_avg = 0\n",
        "\n",
        "print(f'Distance weighted by population output saved to {output_path}')\n",
        "print(f'Total weighted average distance across all URCA categories and all countries: {total_dist_avg}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mK4j9jnI02tH",
        "outputId": "02556842-ce72-4eba-9908-bcf0b65ca718"
      },
      "id": "mK4j9jnI02tH",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Distance weighted by population output saved to /content/drive/My Drive/Rurban/Dists/dist_output_df.csv\n",
            "Total weighted average distance across all URCA categories and all countries: 19.718453267296358\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "display(dist_output_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "qBsRqltxCFxZ",
        "outputId": "2e7f0c96-9106-47dc-c7a2-ed951c346fac"
      },
      "id": "qBsRqltxCFxZ",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                     Country                  1                  2  \\\n",
              "0                     Angola                0.0                0.0   \n",
              "1                      Benin                0.0                0.0   \n",
              "2                   Botswana                0.0                0.0   \n",
              "3               Burkina Faso                0.0                1.0   \n",
              "4                    Burundi                0.0                0.0   \n",
              "5                   Cameroon                0.0                1.0   \n",
              "6   Central African Republic                0.0  1.085346354036317   \n",
              "7                       Chad                0.0                0.0   \n",
              "8                        DRC 1.5763321888926494 1.7807484685882538   \n",
              "9                        ROC                0.0                0.0   \n",
              "10                       CDI                0.0                0.0   \n",
              "11         Equatorial Guinea                0.0                0.0   \n",
              "12                   Eritrea                0.0                0.0   \n",
              "13                 Swaziland                0.0                0.0   \n",
              "14                  Ethiopia                0.0                0.0   \n",
              "15                     Gabon                0.0                0.0   \n",
              "16                    Gambia                0.0                0.0   \n",
              "17                     Ghana                0.0                0.0   \n",
              "18                    Guinea                0.0                1.0   \n",
              "19             Guinea-Bissau                0.0                0.0   \n",
              "20                     Kenya                0.0                0.0   \n",
              "21                   Lesotho                0.0                0.0   \n",
              "22                   Liberia                0.0                1.0   \n",
              "23                Madagascar                0.0                1.0   \n",
              "24                    Malawi                0.0                0.0   \n",
              "25                      Mali                0.0                0.0   \n",
              "26                Mauritania                0.0                0.0   \n",
              "27                Mozambique                0.0                1.0   \n",
              "28                   Namibia                0.0                0.0   \n",
              "29                     Niger                0.0                0.0   \n",
              "30                   Nigeria                0.0  3.873615241716276   \n",
              "31                    Rwanda                0.0                0.0   \n",
              "32                   Senegal                0.0                1.0   \n",
              "33              Sierra Leone                0.0                1.0   \n",
              "34                   Somalia                0.0                0.0   \n",
              "35              South Africa                0.0                0.0   \n",
              "36               South Sudan                0.0                0.0   \n",
              "37                     Sudan                0.0                0.0   \n",
              "38                  Tanzania                0.0                0.0   \n",
              "39                      Togo                0.0                0.0   \n",
              "40                    Uganda                0.0                0.0   \n",
              "41                    Zambia                0.0                0.0   \n",
              "42                  Zimbabwe                0.0                1.0   \n",
              "\n",
              "                    3                  4                  5  \\\n",
              "0                 0.0 1.0583707573612653 14.853175173799052   \n",
              "1                 0.0                0.0 1.0469347100799682   \n",
              "2                 0.0                0.0                0.0   \n",
              "3                 0.0                0.0 43.376844066570875   \n",
              "4  1.4465296829661503                0.0                1.0   \n",
              "5                 1.0 1.0617277210315126 10.215129729982959   \n",
              "6                 0.0                0.0                0.0   \n",
              "7                 0.0 2.7457574572521493 40.900582525251735   \n",
              "8  1.6198112738736046 15.130379523814641  49.75717121044049   \n",
              "9                 0.0 1.1780936911252369 2.0560739647328474   \n",
              "10                0.0                0.0                0.0   \n",
              "11                0.0                0.0                0.0   \n",
              "12                0.0 19.109546204654023 22.493792411075148   \n",
              "13                0.0                0.0                0.0   \n",
              "14                0.0 18.641277682240403 15.722162555024095   \n",
              "15                0.0                0.0                0.0   \n",
              "16                0.0                0.0                0.0   \n",
              "17                0.0                0.0                0.0   \n",
              "18                0.0 1.1979174148339637  1.719918872335234   \n",
              "19 1.1194829146087548                0.0                0.0   \n",
              "20                0.0                0.0  1.055102710390637   \n",
              "21                0.0                0.0                0.0   \n",
              "22                0.0                0.0                0.0   \n",
              "23                0.0                0.0  4.569772472310126   \n",
              "24                0.0                1.0                0.0   \n",
              "25                0.0                0.0                0.0   \n",
              "26                0.0                0.0                0.0   \n",
              "27                0.0                0.0  19.76630423134991   \n",
              "28                0.0                0.0                0.0   \n",
              "29                0.0 1.2370860683136309  49.30680949854753   \n",
              "30 2.8722863390014823 15.854824715638637  17.04378930984431   \n",
              "31 1.1439567930830459                0.0                1.0   \n",
              "32                0.0                1.0 1.0423345487520168   \n",
              "33                0.0 1.1813939332009649  1.336837254246553   \n",
              "34                0.0                1.0  29.16670510829201   \n",
              "35                0.0                0.0                0.0   \n",
              "36   34.5123897798485  2.700461922826717 25.548453000893552   \n",
              "37  4.274970161422784  9.265959928288085 28.591919810303512   \n",
              "38                1.0                1.0                1.0   \n",
              "39                0.0                0.0                1.0   \n",
              "40                0.0                0.0 1.1108195680479274   \n",
              "41 1.0406952598442287                1.0  9.098623162008256   \n",
              "42                1.0                0.0  2.556112378142997   \n",
              "\n",
              "                    6                  7                  8  \\\n",
              "0   31.45288754639633  42.98744314485136 2.9033809876513716   \n",
              "1    4.59048965698202   6.11250574962573 1.1397758917570566   \n",
              "2                 1.0                1.0                0.0   \n",
              "3  19.083743685541386 23.806914641034464                0.0   \n",
              "4   7.017167340299953  9.091464737308051                0.0   \n",
              "5   6.906879523488299 17.330272822560257                0.0   \n",
              "6   35.60514393771672  33.52784004993582                0.0   \n",
              "7  38.796764042860794  59.65596711901412                0.0   \n",
              "8   44.54858440435716 48.998111385057555  8.323812188963734   \n",
              "9   1.209012600320744 21.276891307128096 2.7725943110442004   \n",
              "10                1.0  7.196009817567282                0.0   \n",
              "11                0.0                0.0                0.0   \n",
              "12 14.001970023809731  16.47634006513998                0.0   \n",
              "13                0.0                0.0                0.0   \n",
              "14 19.141226325978735 18.920367782957836                0.0   \n",
              "15  1.246435422941454 2.3717000596314266                0.0   \n",
              "16 2.4283339516844418 1.2976182721088727                0.0   \n",
              "17                1.0  1.010614220463016                0.0   \n",
              "18  16.65188464569555  23.45867691110663                0.0   \n",
              "19 1.3025673336147554 14.303810741283183                0.0   \n",
              "20 1.2673375158448463 13.387295931037544                0.0   \n",
              "21                0.0 11.672850106702828                0.0   \n",
              "22    1.4761617128554   7.37635504714466                0.0   \n",
              "23 10.124427296823535  16.67359672485751                0.0   \n",
              "24 1.0283812395101608                1.0                0.0   \n",
              "25                1.0  35.71918132991277                0.0   \n",
              "26 2.9569768499516322  9.475520241478083                0.0   \n",
              "27 17.087065672688034 15.689228766005048                0.0   \n",
              "28                0.0                0.0                0.0   \n",
              "29 17.251559641749193  21.59667947474758                0.0   \n",
              "30 12.866825455318697  13.33398109850715  3.731330626569487   \n",
              "31 1.0774551979669156 4.9938535357313825                0.0   \n",
              "32 1.4273913544888681   8.99441034704032                0.0   \n",
              "33 10.516622693774748 10.862339446919336                0.0   \n",
              "34  38.73250729883788  33.24099846059371                0.0   \n",
              "35                1.0                1.0 2.0337003427792752   \n",
              "36  41.82825830309309  42.74734826932327                0.0   \n",
              "37  32.82445846255945 28.219696769458242  3.149531446097088   \n",
              "38  22.58300829909722    15.164585600643  4.399879962206638   \n",
              "39  2.295982872438479  8.649261935654879                0.0   \n",
              "40  7.148560643960963 12.408038728318328                0.0   \n",
              "41  18.76905357720913   32.7463298677485                0.0   \n",
              "42  18.74529432179828 15.851500312631304                0.0   \n",
              "\n",
              "                    9  ...                 22                 23  \\\n",
              "0                 0.0  ...  39.30735070498774                0.0   \n",
              "1    3.36499667082605  ...                0.0                0.0   \n",
              "2                 0.0  ...                0.0                0.0   \n",
              "3  10.956503509473928  ...                0.0 25.423590961471394   \n",
              "4                 0.0  ...                0.0                0.0   \n",
              "5   7.891694798314107  ...                0.0  7.493187692137451   \n",
              "6  10.658494167777949  ...                0.0  20.57274906647495   \n",
              "7   4.077593158481093  ...                0.0 24.323474736001096   \n",
              "8   11.46541463041959  ...  53.40743660569291  37.73022328694391   \n",
              "9   8.780411814598665  ... 11.622244555663379 18.074607139629713   \n",
              "10 2.7989695905479683  ...                0.0  5.393869601353389   \n",
              "11                0.0  ...                0.0                0.0   \n",
              "12  6.209090455225182  ...                0.0 13.946085048331252   \n",
              "13                1.0  ...                0.0                0.0   \n",
              "14  6.414970649674813  ...                0.0  12.47055780655319   \n",
              "15                0.0  ...                0.0                0.0   \n",
              "16                0.0  ...                0.0                0.0   \n",
              "17  2.105137680801453  ...                0.0  5.846508710651303   \n",
              "18  4.905570601418415  ...                0.0 10.693477303041707   \n",
              "19                0.0  ...                0.0                0.0   \n",
              "20 3.2752510339557914  ...                0.0  10.46749541376307   \n",
              "21                0.0  ...                0.0                1.0   \n",
              "22 6.7823965641590815  ...                0.0  38.20458108892885   \n",
              "23  5.988538456475555  ...                0.0 37.064471185933215   \n",
              "24                0.0  ...                0.0                0.0   \n",
              "25 10.644725206057188  ...                0.0  34.70963298556674   \n",
              "26 3.9261799812259457  ...                0.0  7.654086511149819   \n",
              "27  3.928333961208892  ...                0.0  8.893932831319583   \n",
              "28                0.0  ...                0.0                0.0   \n",
              "29   9.02806104807605  ...                0.0  24.45142689737051   \n",
              "30  7.115009311503158  ...  6.139802945263895 15.480576981494101   \n",
              "31  5.497596200788778  ...                0.0                0.0   \n",
              "32   4.86605909705294  ...                0.0 24.410856349369627   \n",
              "33 6.2924336814411035  ...                0.0  11.60865395802955   \n",
              "34  5.483827190974118  ...                0.0 55.301386057854934   \n",
              "35  2.731666572987561  ...  6.191101104206462  5.433914590035067   \n",
              "36                0.0  ...                0.0                0.0   \n",
              "37                0.0  ... 12.617741176406973                0.0   \n",
              "38                0.0  ...  29.51439481498941  8.296777564259415   \n",
              "39 3.9411866207714192  ...                0.0 16.797795865181335   \n",
              "40  4.531642523424684  ...                0.0 3.2360356303544022   \n",
              "41  5.451036154153626  ...                0.0  16.91670799259084   \n",
              "42 2.4741800338533406  ...                0.0  8.931897378589397   \n",
              "\n",
              "                   24                 25                 26  \\\n",
              "0   23.56400232050193  38.16440544504604  32.42813089612986   \n",
              "1                 0.0 16.775472641438753 27.039255400736053   \n",
              "2                 0.0                0.0 16.884055120204565   \n",
              "3  20.020990829070694  3.906206699580159 23.509009694050384   \n",
              "4   6.526889017828154                0.0  4.178958720442223   \n",
              "5   16.10985972710173  25.91623699093387 21.231386916140952   \n",
              "6                 0.0 1.4348746173664757 12.691769441111012   \n",
              "7   4.273492747461063  28.35562517186423 49.461307434142704   \n",
              "8  36.011552557282684  57.61993405981059  72.53748404289665   \n",
              "9  14.435143293813177 27.775383756114913 27.515631556081914   \n",
              "10  9.548704445356249 11.004524266558152 10.239312399720493   \n",
              "11    4.6428289028482                0.0  8.123515322435985   \n",
              "12  18.38836361288581  34.07989592387043 20.648531986914552   \n",
              "13                0.0                0.0 1.3719077603583145   \n",
              "14 4.5383847918128675 21.629694463127343   25.4548125232167   \n",
              "15  8.669457959409183                0.0 1.1510834894877526   \n",
              "16 2.5763339752443932                0.0                1.0   \n",
              "17  6.811010560221864 13.791636791587813  6.110530850038175   \n",
              "18                0.0 36.296687814770955  13.38686768931358   \n",
              "19 4.7388858836731655   7.41796604787488 2.1278662226370693   \n",
              "20 1.0728907108060084 11.908747079903685  31.56907666093074   \n",
              "21                0.0 1.4619461287308457 10.329439738763847   \n",
              "22                0.0                0.0 1.8902550649293401   \n",
              "23                0.0                0.0  30.22443032039235   \n",
              "24  4.263909856294664  2.293605537578049 3.6989591180980215   \n",
              "25                0.0 36.429181143576585 29.135405317918003   \n",
              "26                0.0                0.0 10.331958758314405   \n",
              "27 21.422469790028764  23.19895823795014  23.12377423542395   \n",
              "28                0.0 14.266085412773403  17.36839754106873   \n",
              "29  39.69045631104074  22.38371596607445  31.12174492329163   \n",
              "30 15.101456091902321 18.192656781124672  22.99961839963283   \n",
              "31  2.485198831318933                0.0 2.0769174938282156   \n",
              "32  4.511336423149668  44.43703707829493  31.74675673640639   \n",
              "33                0.0 13.400999616904508 15.524754344758035   \n",
              "34 25.836311850605984   38.3105018717265 57.847020933664936   \n",
              "35  9.852063899497152  16.91613920475373  6.855010723407955   \n",
              "36 31.612114379355134 36.196381680877884 41.397171768163965   \n",
              "37 21.914146492158316  29.49397159883118 28.904234822760202   \n",
              "38  26.55585418581183  22.35630621245806  21.42824957496062   \n",
              "39                0.0                0.0 17.230288741482035   \n",
              "40   2.08737650471869  8.375542986730347  7.635562661094446   \n",
              "41  30.13078670632391 26.178947724584518 30.569597492310177   \n",
              "42 16.986781314495325 11.800937065086655 15.854937795404334   \n",
              "\n",
              "                   27                 28                 29  \\\n",
              "0   35.76627959257785 43.195525842365285  31.53593227231475   \n",
              "1   21.84819145491615  27.37488633825388                0.0   \n",
              "2  26.367712605715525 21.870351037485687                1.0   \n",
              "3   19.51302685787203 22.024160144304037 1.1067183843521293   \n",
              "4  3.2426738658614793 1.8464460317055131                0.0   \n",
              "5   28.54967895772421 24.896476324028214 18.393998589882607   \n",
              "6    38.4183011811059  48.78745908343801  72.19203942707419   \n",
              "7  38.395308169544364 50.568778752458805  60.90780057805837   \n",
              "8  62.391859182402875  71.64439209151024  96.77726799065834   \n",
              "9  23.021961244883137 27.226634364923687 11.556352618461348   \n",
              "10  9.611032043659764 11.573551182571192                0.0   \n",
              "11  8.101535789762748  9.054811048778836                0.0   \n",
              "12  36.86917641402191 24.102936240471724 21.155801900296947   \n",
              "13 2.0247381614041884 2.2738126447099933                0.0   \n",
              "14 29.822062424490497 40.692245867795755  34.65793056976085   \n",
              "15  13.18726121284415 15.954402453308502 16.065622772640406   \n",
              "16                0.0                0.0                0.0   \n",
              "17 13.758604493933301 10.963892505041253                0.0   \n",
              "18 22.547657975558828  19.15135717502903                1.0   \n",
              "19 15.827352625510603 13.408257424134371  4.271872357851372   \n",
              "20    23.329533391847  28.50184284434065  75.07828718635567   \n",
              "21 1.4883062160587779 12.712774172453376                0.0   \n",
              "22 23.655862207381308 28.522718949764588 20.580690735896653   \n",
              "23 32.735871513766426 31.743490356800525  42.67660134694557   \n",
              "24   2.29020959584086 10.334989502334912                0.0   \n",
              "25 30.076075135455685  37.81946759194136 15.624649614263644   \n",
              "26 24.407309075106923 23.797801128033164  20.93331282270353   \n",
              "27 29.878552675511024 27.709479002022405 32.953128821032415   \n",
              "28 17.089685367435724 16.946747403808093                0.0   \n",
              "29 34.296483921085276 40.210710138213194   60.9417957045765   \n",
              "30 18.124734985782823 20.888302984388766 2.0385297893547745   \n",
              "31 1.1317502131221293 3.1066771652282443                0.0   \n",
              "32  27.56530822801111 30.116181197473438  82.09750300709516   \n",
              "33 15.787931946397137  19.20693650690494                0.0   \n",
              "34  92.77681749467743  59.04021276046856 52.191367907749516   \n",
              "35  6.176708196484487 12.530790432033445                0.0   \n",
              "36 48.146327431923055 49.982232334505625  58.93566521247332   \n",
              "37  33.92095134331323  32.16534164046949 51.853091084719914   \n",
              "38 27.811446877452067 23.558963518700523  23.05676916122574   \n",
              "39 16.441348092427557 14.673362412703074                0.0   \n",
              "40 11.058673142193292 18.536638717132494 3.4357405799883076   \n",
              "41 30.954057607086206  35.59047160990514 25.746948615966566   \n",
              "42  17.82674399652098 22.590824920403488 11.068900519297665   \n",
              "\n",
              "                   30  Average_Weighted_Distance  \n",
              "0   48.96094479579002         20.924065395947913  \n",
              "1  22.336436422499233          8.745867373474415  \n",
              "2  33.237926472021734          5.528805250943234  \n",
              "3   20.87614282857802         13.168422360613686  \n",
              "4  1.6166411151278468         3.0217258253950456  \n",
              "5  25.097634963441646         13.562099324349713  \n",
              "6  50.015789298344124          16.63357500112447  \n",
              "7   60.84340939852576         25.956410672946202  \n",
              "8   73.41231863626102          42.95259102477739  \n",
              "9   31.06926544011234         12.513676778370101  \n",
              "10 16.340981336730444          5.204943299655051  \n",
              "11 10.887243203444946          2.432033276653762  \n",
              "12 26.632924330342604         15.931799229095294  \n",
              "13  1.249113128294217         0.8481787235437251  \n",
              "14  35.36448240807822          16.85761328155147  \n",
              "15 23.187786451731892         4.2808977240895425  \n",
              "16                0.0          1.743909772866697  \n",
              "17 14.349060827770858          4.502559955754924  \n",
              "18 17.889191097373374         12.127916431462515  \n",
              "19  6.848371236795015          5.677792630649012  \n",
              "20   33.9722837707287         11.195462702880777  \n",
              "21 10.071325822454307         3.1784884007914513  \n",
              "22  33.40915507635913          8.072987693950719  \n",
              "23  43.66302692587329         13.170503236097183  \n",
              "24  4.332447104214164         3.4965568119681403  \n",
              "25  54.14920910054576           16.6034938422605  \n",
              "26   32.8222449411717          7.576020796330456  \n",
              "27  29.70239825597598         13.811535538774486  \n",
              "28 22.444399579334018          5.097109970887476  \n",
              "29  48.41612111996262         21.028226965923746  \n",
              "30 13.812276348201342          13.65006579456577  \n",
              "31 3.1236156402647133         2.3846454338528145  \n",
              "32 37.484847696968004           14.2370511362312  \n",
              "33  7.797408522618599          9.166278882053055  \n",
              "34  52.19921376249389         30.327933983689636  \n",
              "35 13.558191548696236          4.964234849630149  \n",
              "36 54.635786735417135          27.50657736767014  \n",
              "37 42.572229953853025         19.994962175888183  \n",
              "38 27.804488022201745         13.128296927451174  \n",
              "39 16.276185650033575          5.531810607506406  \n",
              "40  10.08700307379699          8.237034530228609  \n",
              "41  40.00049896104492         16.803656873430505  \n",
              "42  21.55249756335931          9.118491247346867  \n",
              "\n",
              "[43 rows x 32 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-722a06e5-6e17-4318-832b-972afaa7ce9f\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Country</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>...</th>\n",
              "      <th>22</th>\n",
              "      <th>23</th>\n",
              "      <th>24</th>\n",
              "      <th>25</th>\n",
              "      <th>26</th>\n",
              "      <th>27</th>\n",
              "      <th>28</th>\n",
              "      <th>29</th>\n",
              "      <th>30</th>\n",
              "      <th>Average_Weighted_Distance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Angola</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0583707573612653</td>\n",
              "      <td>14.853175173799052</td>\n",
              "      <td>31.45288754639633</td>\n",
              "      <td>42.98744314485136</td>\n",
              "      <td>2.9033809876513716</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>39.30735070498774</td>\n",
              "      <td>0.0</td>\n",
              "      <td>23.56400232050193</td>\n",
              "      <td>38.16440544504604</td>\n",
              "      <td>32.42813089612986</td>\n",
              "      <td>35.76627959257785</td>\n",
              "      <td>43.195525842365285</td>\n",
              "      <td>31.53593227231475</td>\n",
              "      <td>48.96094479579002</td>\n",
              "      <td>20.924065395947913</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Benin</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0469347100799682</td>\n",
              "      <td>4.59048965698202</td>\n",
              "      <td>6.11250574962573</td>\n",
              "      <td>1.1397758917570566</td>\n",
              "      <td>3.36499667082605</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>16.775472641438753</td>\n",
              "      <td>27.039255400736053</td>\n",
              "      <td>21.84819145491615</td>\n",
              "      <td>27.37488633825388</td>\n",
              "      <td>0.0</td>\n",
              "      <td>22.336436422499233</td>\n",
              "      <td>8.745867373474415</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Botswana</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>16.884055120204565</td>\n",
              "      <td>26.367712605715525</td>\n",
              "      <td>21.870351037485687</td>\n",
              "      <td>1.0</td>\n",
              "      <td>33.237926472021734</td>\n",
              "      <td>5.528805250943234</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Burkina Faso</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>43.376844066570875</td>\n",
              "      <td>19.083743685541386</td>\n",
              "      <td>23.806914641034464</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.956503509473928</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>25.423590961471394</td>\n",
              "      <td>20.020990829070694</td>\n",
              "      <td>3.906206699580159</td>\n",
              "      <td>23.509009694050384</td>\n",
              "      <td>19.51302685787203</td>\n",
              "      <td>22.024160144304037</td>\n",
              "      <td>1.1067183843521293</td>\n",
              "      <td>20.87614282857802</td>\n",
              "      <td>13.168422360613686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Burundi</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.4465296829661503</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.017167340299953</td>\n",
              "      <td>9.091464737308051</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.526889017828154</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.178958720442223</td>\n",
              "      <td>3.2426738658614793</td>\n",
              "      <td>1.8464460317055131</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.6166411151278468</td>\n",
              "      <td>3.0217258253950456</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Cameroon</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0617277210315126</td>\n",
              "      <td>10.215129729982959</td>\n",
              "      <td>6.906879523488299</td>\n",
              "      <td>17.330272822560257</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.891694798314107</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.493187692137451</td>\n",
              "      <td>16.10985972710173</td>\n",
              "      <td>25.91623699093387</td>\n",
              "      <td>21.231386916140952</td>\n",
              "      <td>28.54967895772421</td>\n",
              "      <td>24.896476324028214</td>\n",
              "      <td>18.393998589882607</td>\n",
              "      <td>25.097634963441646</td>\n",
              "      <td>13.562099324349713</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Central African Republic</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.085346354036317</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>35.60514393771672</td>\n",
              "      <td>33.52784004993582</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.658494167777949</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>20.57274906647495</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.4348746173664757</td>\n",
              "      <td>12.691769441111012</td>\n",
              "      <td>38.4183011811059</td>\n",
              "      <td>48.78745908343801</td>\n",
              "      <td>72.19203942707419</td>\n",
              "      <td>50.015789298344124</td>\n",
              "      <td>16.63357500112447</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Chad</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.7457574572521493</td>\n",
              "      <td>40.900582525251735</td>\n",
              "      <td>38.796764042860794</td>\n",
              "      <td>59.65596711901412</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.077593158481093</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>24.323474736001096</td>\n",
              "      <td>4.273492747461063</td>\n",
              "      <td>28.35562517186423</td>\n",
              "      <td>49.461307434142704</td>\n",
              "      <td>38.395308169544364</td>\n",
              "      <td>50.568778752458805</td>\n",
              "      <td>60.90780057805837</td>\n",
              "      <td>60.84340939852576</td>\n",
              "      <td>25.956410672946202</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>DRC</td>\n",
              "      <td>1.5763321888926494</td>\n",
              "      <td>1.7807484685882538</td>\n",
              "      <td>1.6198112738736046</td>\n",
              "      <td>15.130379523814641</td>\n",
              "      <td>49.75717121044049</td>\n",
              "      <td>44.54858440435716</td>\n",
              "      <td>48.998111385057555</td>\n",
              "      <td>8.323812188963734</td>\n",
              "      <td>11.46541463041959</td>\n",
              "      <td>...</td>\n",
              "      <td>53.40743660569291</td>\n",
              "      <td>37.73022328694391</td>\n",
              "      <td>36.011552557282684</td>\n",
              "      <td>57.61993405981059</td>\n",
              "      <td>72.53748404289665</td>\n",
              "      <td>62.391859182402875</td>\n",
              "      <td>71.64439209151024</td>\n",
              "      <td>96.77726799065834</td>\n",
              "      <td>73.41231863626102</td>\n",
              "      <td>42.95259102477739</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>ROC</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.1780936911252369</td>\n",
              "      <td>2.0560739647328474</td>\n",
              "      <td>1.209012600320744</td>\n",
              "      <td>21.276891307128096</td>\n",
              "      <td>2.7725943110442004</td>\n",
              "      <td>8.780411814598665</td>\n",
              "      <td>...</td>\n",
              "      <td>11.622244555663379</td>\n",
              "      <td>18.074607139629713</td>\n",
              "      <td>14.435143293813177</td>\n",
              "      <td>27.775383756114913</td>\n",
              "      <td>27.515631556081914</td>\n",
              "      <td>23.021961244883137</td>\n",
              "      <td>27.226634364923687</td>\n",
              "      <td>11.556352618461348</td>\n",
              "      <td>31.06926544011234</td>\n",
              "      <td>12.513676778370101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>CDI</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.196009817567282</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.7989695905479683</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.393869601353389</td>\n",
              "      <td>9.548704445356249</td>\n",
              "      <td>11.004524266558152</td>\n",
              "      <td>10.239312399720493</td>\n",
              "      <td>9.611032043659764</td>\n",
              "      <td>11.573551182571192</td>\n",
              "      <td>0.0</td>\n",
              "      <td>16.340981336730444</td>\n",
              "      <td>5.204943299655051</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Equatorial Guinea</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.6428289028482</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.123515322435985</td>\n",
              "      <td>8.101535789762748</td>\n",
              "      <td>9.054811048778836</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.887243203444946</td>\n",
              "      <td>2.432033276653762</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Eritrea</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>19.109546204654023</td>\n",
              "      <td>22.493792411075148</td>\n",
              "      <td>14.001970023809731</td>\n",
              "      <td>16.47634006513998</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.209090455225182</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>13.946085048331252</td>\n",
              "      <td>18.38836361288581</td>\n",
              "      <td>34.07989592387043</td>\n",
              "      <td>20.648531986914552</td>\n",
              "      <td>36.86917641402191</td>\n",
              "      <td>24.102936240471724</td>\n",
              "      <td>21.155801900296947</td>\n",
              "      <td>26.632924330342604</td>\n",
              "      <td>15.931799229095294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Swaziland</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.3719077603583145</td>\n",
              "      <td>2.0247381614041884</td>\n",
              "      <td>2.2738126447099933</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.249113128294217</td>\n",
              "      <td>0.8481787235437251</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Ethiopia</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18.641277682240403</td>\n",
              "      <td>15.722162555024095</td>\n",
              "      <td>19.141226325978735</td>\n",
              "      <td>18.920367782957836</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.414970649674813</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12.47055780655319</td>\n",
              "      <td>4.5383847918128675</td>\n",
              "      <td>21.629694463127343</td>\n",
              "      <td>25.4548125232167</td>\n",
              "      <td>29.822062424490497</td>\n",
              "      <td>40.692245867795755</td>\n",
              "      <td>34.65793056976085</td>\n",
              "      <td>35.36448240807822</td>\n",
              "      <td>16.85761328155147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Gabon</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.246435422941454</td>\n",
              "      <td>2.3717000596314266</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.669457959409183</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.1510834894877526</td>\n",
              "      <td>13.18726121284415</td>\n",
              "      <td>15.954402453308502</td>\n",
              "      <td>16.065622772640406</td>\n",
              "      <td>23.187786451731892</td>\n",
              "      <td>4.2808977240895425</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Gambia</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.4283339516844418</td>\n",
              "      <td>1.2976182721088727</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.5763339752443932</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.743909772866697</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Ghana</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.010614220463016</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.105137680801453</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.846508710651303</td>\n",
              "      <td>6.811010560221864</td>\n",
              "      <td>13.791636791587813</td>\n",
              "      <td>6.110530850038175</td>\n",
              "      <td>13.758604493933301</td>\n",
              "      <td>10.963892505041253</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14.349060827770858</td>\n",
              "      <td>4.502559955754924</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Guinea</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.1979174148339637</td>\n",
              "      <td>1.719918872335234</td>\n",
              "      <td>16.65188464569555</td>\n",
              "      <td>23.45867691110663</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.905570601418415</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.693477303041707</td>\n",
              "      <td>0.0</td>\n",
              "      <td>36.296687814770955</td>\n",
              "      <td>13.38686768931358</td>\n",
              "      <td>22.547657975558828</td>\n",
              "      <td>19.15135717502903</td>\n",
              "      <td>1.0</td>\n",
              "      <td>17.889191097373374</td>\n",
              "      <td>12.127916431462515</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Guinea-Bissau</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.1194829146087548</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.3025673336147554</td>\n",
              "      <td>14.303810741283183</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.7388858836731655</td>\n",
              "      <td>7.41796604787488</td>\n",
              "      <td>2.1278662226370693</td>\n",
              "      <td>15.827352625510603</td>\n",
              "      <td>13.408257424134371</td>\n",
              "      <td>4.271872357851372</td>\n",
              "      <td>6.848371236795015</td>\n",
              "      <td>5.677792630649012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Kenya</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.055102710390637</td>\n",
              "      <td>1.2673375158448463</td>\n",
              "      <td>13.387295931037544</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.2752510339557914</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.46749541376307</td>\n",
              "      <td>1.0728907108060084</td>\n",
              "      <td>11.908747079903685</td>\n",
              "      <td>31.56907666093074</td>\n",
              "      <td>23.329533391847</td>\n",
              "      <td>28.50184284434065</td>\n",
              "      <td>75.07828718635567</td>\n",
              "      <td>33.9722837707287</td>\n",
              "      <td>11.195462702880777</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Lesotho</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.672850106702828</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.4619461287308457</td>\n",
              "      <td>10.329439738763847</td>\n",
              "      <td>1.4883062160587779</td>\n",
              "      <td>12.712774172453376</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.071325822454307</td>\n",
              "      <td>3.1784884007914513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Liberia</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.4761617128554</td>\n",
              "      <td>7.37635504714466</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.7823965641590815</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>38.20458108892885</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.8902550649293401</td>\n",
              "      <td>23.655862207381308</td>\n",
              "      <td>28.522718949764588</td>\n",
              "      <td>20.580690735896653</td>\n",
              "      <td>33.40915507635913</td>\n",
              "      <td>8.072987693950719</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Madagascar</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.569772472310126</td>\n",
              "      <td>10.124427296823535</td>\n",
              "      <td>16.67359672485751</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.988538456475555</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37.064471185933215</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30.22443032039235</td>\n",
              "      <td>32.735871513766426</td>\n",
              "      <td>31.743490356800525</td>\n",
              "      <td>42.67660134694557</td>\n",
              "      <td>43.66302692587329</td>\n",
              "      <td>13.170503236097183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Malawi</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0283812395101608</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.263909856294664</td>\n",
              "      <td>2.293605537578049</td>\n",
              "      <td>3.6989591180980215</td>\n",
              "      <td>2.29020959584086</td>\n",
              "      <td>10.334989502334912</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.332447104214164</td>\n",
              "      <td>3.4965568119681403</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Mali</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>35.71918132991277</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.644725206057188</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>34.70963298556674</td>\n",
              "      <td>0.0</td>\n",
              "      <td>36.429181143576585</td>\n",
              "      <td>29.135405317918003</td>\n",
              "      <td>30.076075135455685</td>\n",
              "      <td>37.81946759194136</td>\n",
              "      <td>15.624649614263644</td>\n",
              "      <td>54.14920910054576</td>\n",
              "      <td>16.6034938422605</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Mauritania</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.9569768499516322</td>\n",
              "      <td>9.475520241478083</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.9261799812259457</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.654086511149819</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.331958758314405</td>\n",
              "      <td>24.407309075106923</td>\n",
              "      <td>23.797801128033164</td>\n",
              "      <td>20.93331282270353</td>\n",
              "      <td>32.8222449411717</td>\n",
              "      <td>7.576020796330456</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Mozambique</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>19.76630423134991</td>\n",
              "      <td>17.087065672688034</td>\n",
              "      <td>15.689228766005048</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.928333961208892</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.893932831319583</td>\n",
              "      <td>21.422469790028764</td>\n",
              "      <td>23.19895823795014</td>\n",
              "      <td>23.12377423542395</td>\n",
              "      <td>29.878552675511024</td>\n",
              "      <td>27.709479002022405</td>\n",
              "      <td>32.953128821032415</td>\n",
              "      <td>29.70239825597598</td>\n",
              "      <td>13.811535538774486</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Namibia</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14.266085412773403</td>\n",
              "      <td>17.36839754106873</td>\n",
              "      <td>17.089685367435724</td>\n",
              "      <td>16.946747403808093</td>\n",
              "      <td>0.0</td>\n",
              "      <td>22.444399579334018</td>\n",
              "      <td>5.097109970887476</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Niger</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.2370860683136309</td>\n",
              "      <td>49.30680949854753</td>\n",
              "      <td>17.251559641749193</td>\n",
              "      <td>21.59667947474758</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.02806104807605</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>24.45142689737051</td>\n",
              "      <td>39.69045631104074</td>\n",
              "      <td>22.38371596607445</td>\n",
              "      <td>31.12174492329163</td>\n",
              "      <td>34.296483921085276</td>\n",
              "      <td>40.210710138213194</td>\n",
              "      <td>60.9417957045765</td>\n",
              "      <td>48.41612111996262</td>\n",
              "      <td>21.028226965923746</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Nigeria</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.873615241716276</td>\n",
              "      <td>2.8722863390014823</td>\n",
              "      <td>15.854824715638637</td>\n",
              "      <td>17.04378930984431</td>\n",
              "      <td>12.866825455318697</td>\n",
              "      <td>13.33398109850715</td>\n",
              "      <td>3.731330626569487</td>\n",
              "      <td>7.115009311503158</td>\n",
              "      <td>...</td>\n",
              "      <td>6.139802945263895</td>\n",
              "      <td>15.480576981494101</td>\n",
              "      <td>15.101456091902321</td>\n",
              "      <td>18.192656781124672</td>\n",
              "      <td>22.99961839963283</td>\n",
              "      <td>18.124734985782823</td>\n",
              "      <td>20.888302984388766</td>\n",
              "      <td>2.0385297893547745</td>\n",
              "      <td>13.812276348201342</td>\n",
              "      <td>13.65006579456577</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Rwanda</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.1439567930830459</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0774551979669156</td>\n",
              "      <td>4.9938535357313825</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.497596200788778</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.485198831318933</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0769174938282156</td>\n",
              "      <td>1.1317502131221293</td>\n",
              "      <td>3.1066771652282443</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.1236156402647133</td>\n",
              "      <td>2.3846454338528145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Senegal</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0423345487520168</td>\n",
              "      <td>1.4273913544888681</td>\n",
              "      <td>8.99441034704032</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.86605909705294</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>24.410856349369627</td>\n",
              "      <td>4.511336423149668</td>\n",
              "      <td>44.43703707829493</td>\n",
              "      <td>31.74675673640639</td>\n",
              "      <td>27.56530822801111</td>\n",
              "      <td>30.116181197473438</td>\n",
              "      <td>82.09750300709516</td>\n",
              "      <td>37.484847696968004</td>\n",
              "      <td>14.2370511362312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>Sierra Leone</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.1813939332009649</td>\n",
              "      <td>1.336837254246553</td>\n",
              "      <td>10.516622693774748</td>\n",
              "      <td>10.862339446919336</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.2924336814411035</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.60865395802955</td>\n",
              "      <td>0.0</td>\n",
              "      <td>13.400999616904508</td>\n",
              "      <td>15.524754344758035</td>\n",
              "      <td>15.787931946397137</td>\n",
              "      <td>19.20693650690494</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.797408522618599</td>\n",
              "      <td>9.166278882053055</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Somalia</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>29.16670510829201</td>\n",
              "      <td>38.73250729883788</td>\n",
              "      <td>33.24099846059371</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.483827190974118</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>55.301386057854934</td>\n",
              "      <td>25.836311850605984</td>\n",
              "      <td>38.3105018717265</td>\n",
              "      <td>57.847020933664936</td>\n",
              "      <td>92.77681749467743</td>\n",
              "      <td>59.04021276046856</td>\n",
              "      <td>52.191367907749516</td>\n",
              "      <td>52.19921376249389</td>\n",
              "      <td>30.327933983689636</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>South Africa</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0337003427792752</td>\n",
              "      <td>2.731666572987561</td>\n",
              "      <td>...</td>\n",
              "      <td>6.191101104206462</td>\n",
              "      <td>5.433914590035067</td>\n",
              "      <td>9.852063899497152</td>\n",
              "      <td>16.91613920475373</td>\n",
              "      <td>6.855010723407955</td>\n",
              "      <td>6.176708196484487</td>\n",
              "      <td>12.530790432033445</td>\n",
              "      <td>0.0</td>\n",
              "      <td>13.558191548696236</td>\n",
              "      <td>4.964234849630149</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>South Sudan</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>34.5123897798485</td>\n",
              "      <td>2.700461922826717</td>\n",
              "      <td>25.548453000893552</td>\n",
              "      <td>41.82825830309309</td>\n",
              "      <td>42.74734826932327</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>31.612114379355134</td>\n",
              "      <td>36.196381680877884</td>\n",
              "      <td>41.397171768163965</td>\n",
              "      <td>48.146327431923055</td>\n",
              "      <td>49.982232334505625</td>\n",
              "      <td>58.93566521247332</td>\n",
              "      <td>54.635786735417135</td>\n",
              "      <td>27.50657736767014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Sudan</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.274970161422784</td>\n",
              "      <td>9.265959928288085</td>\n",
              "      <td>28.591919810303512</td>\n",
              "      <td>32.82445846255945</td>\n",
              "      <td>28.219696769458242</td>\n",
              "      <td>3.149531446097088</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>12.617741176406973</td>\n",
              "      <td>0.0</td>\n",
              "      <td>21.914146492158316</td>\n",
              "      <td>29.49397159883118</td>\n",
              "      <td>28.904234822760202</td>\n",
              "      <td>33.92095134331323</td>\n",
              "      <td>32.16534164046949</td>\n",
              "      <td>51.853091084719914</td>\n",
              "      <td>42.572229953853025</td>\n",
              "      <td>19.994962175888183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Tanzania</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>22.58300829909722</td>\n",
              "      <td>15.164585600643</td>\n",
              "      <td>4.399879962206638</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>29.51439481498941</td>\n",
              "      <td>8.296777564259415</td>\n",
              "      <td>26.55585418581183</td>\n",
              "      <td>22.35630621245806</td>\n",
              "      <td>21.42824957496062</td>\n",
              "      <td>27.811446877452067</td>\n",
              "      <td>23.558963518700523</td>\n",
              "      <td>23.05676916122574</td>\n",
              "      <td>27.804488022201745</td>\n",
              "      <td>13.128296927451174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>Togo</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.295982872438479</td>\n",
              "      <td>8.649261935654879</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.9411866207714192</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>16.797795865181335</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>17.230288741482035</td>\n",
              "      <td>16.441348092427557</td>\n",
              "      <td>14.673362412703074</td>\n",
              "      <td>0.0</td>\n",
              "      <td>16.276185650033575</td>\n",
              "      <td>5.531810607506406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>Uganda</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.1108195680479274</td>\n",
              "      <td>7.148560643960963</td>\n",
              "      <td>12.408038728318328</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.531642523424684</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.2360356303544022</td>\n",
              "      <td>2.08737650471869</td>\n",
              "      <td>8.375542986730347</td>\n",
              "      <td>7.635562661094446</td>\n",
              "      <td>11.058673142193292</td>\n",
              "      <td>18.536638717132494</td>\n",
              "      <td>3.4357405799883076</td>\n",
              "      <td>10.08700307379699</td>\n",
              "      <td>8.237034530228609</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>Zambia</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0406952598442287</td>\n",
              "      <td>1.0</td>\n",
              "      <td>9.098623162008256</td>\n",
              "      <td>18.76905357720913</td>\n",
              "      <td>32.7463298677485</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.451036154153626</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>16.91670799259084</td>\n",
              "      <td>30.13078670632391</td>\n",
              "      <td>26.178947724584518</td>\n",
              "      <td>30.569597492310177</td>\n",
              "      <td>30.954057607086206</td>\n",
              "      <td>35.59047160990514</td>\n",
              "      <td>25.746948615966566</td>\n",
              "      <td>40.00049896104492</td>\n",
              "      <td>16.803656873430505</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.556112378142997</td>\n",
              "      <td>18.74529432179828</td>\n",
              "      <td>15.851500312631304</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.4741800338533406</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.931897378589397</td>\n",
              "      <td>16.986781314495325</td>\n",
              "      <td>11.800937065086655</td>\n",
              "      <td>15.854937795404334</td>\n",
              "      <td>17.82674399652098</td>\n",
              "      <td>22.590824920403488</td>\n",
              "      <td>11.068900519297665</td>\n",
              "      <td>21.55249756335931</td>\n",
              "      <td>9.118491247346867</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>43 rows × 32 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-722a06e5-6e17-4318-832b-972afaa7ce9f')\"\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "    .colab-df-buttons div {\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    0% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## Estimate distribution of unelectrified within specified radius of urban agglomerations"
      ],
      "metadata": {
        "id": "2FJBO2-MAveC"
      },
      "id": "2FJBO2-MAveC"
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the file paths\n",
        "urban_extents_rel_path = 'Urban Extent Data/AFRICAPOLIS2020.shp'\n",
        "urban_extents_path = os.path.join(fp, urban_extents_rel_path)\n",
        "\n",
        "if os.path.exists(urban_extents_path):\n",
        "    urban_extents = gpd.read_file(urban_extents_path)\n",
        "\n",
        "electrification_raster_path = os.path.join(fp, 'AtlasAI Data/Electrification_0_7_Africa_2015_v2.tif')\n",
        "population_raster_path = os.path.join(fp, 'AtlasAI Data/Population_4_1_Africa_2015.tif')"
      ],
      "metadata": {
        "id": "rUB1fz4TFoce"
      },
      "id": "rUB1fz4TFoce",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the file paths\n",
        "urban_extents_path = os.path.join(fp, 'Urban Extent Data/AFRICAPOLIS2020.shp')\n",
        "\n",
        "if os.path.exists(urban_extents_path):\n",
        "    urban_extents = gpd.read_file(urban_extents_path)\n",
        "\n",
        "# Exclude specified ISO3 codes\n",
        "exclude_iso3 = ['EGY', 'MAR', 'TUN', 'LBY', 'DZA']\n",
        "urban_extents = urban_extents[~urban_extents['ISO3'].isin(exclude_iso3)]\n",
        "\n",
        "# Compute centroids of the urban agglomerations\n",
        "urban_extents['centroid'] = urban_extents.geometry.centroid\n",
        "\n",
        "# Extract longitude and latitude from centroids\n",
        "urban_extents['Longitude'] = urban_extents['centroid'].x\n",
        "urban_extents['Latitude'] = urban_extents['centroid'].y\n",
        "\n",
        "# Prepare a DataFrame to store results\n",
        "results = pd.DataFrame(columns=['agglosName', 'ISO3', 'Pop2015', 'Longitude', 'Latitude', 'Radius_Kilometers', 'Total_Electrified_Population', 'Total_Unelectrified_Population', 'Electrification_Rate'])\n",
        "\n",
        "# Function to calculate population statistics within a buffer radius\n",
        "def calculate_population_stats(centroid, radius_km):\n",
        "    # Convert radius from kilometers to meters (1 kilometer = 1000 meters)\n",
        "    radius_meters = radius_km * 1000\n",
        "\n",
        "    # Create buffer around centroid\n",
        "    buffer = centroid.buffer(radius_meters)\n",
        "\n",
        "    # Mask the population raster with the buffer\n",
        "    with rio.open(population_raster_path) as pop_src:\n",
        "        pop_image, pop_transform = rio.mask.mask(pop_src, [buffer], crop=True)\n",
        "        population_data = pop_image[0]\n",
        "        pop_nodata = pop_src.nodata\n",
        "\n",
        "    # Mask the electrification raster with the buffer\n",
        "    with rio.open(electrification_raster_path) as elec_src:\n",
        "        elec_image, elec_transform = rio.mask.mask(elec_src, [buffer], crop=True)\n",
        "        electrification_data = elec_image[0]\n",
        "        elec_nodata = elec_src.nodata\n",
        "\n",
        "    # Ensure valid data by setting NoData values to zero\n",
        "    if pop_nodata is not None:\n",
        "        population_data = np.where(population_data == pop_nodata, 0, population_data)\n",
        "    if elec_nodata is not None:\n",
        "        electrification_data = np.where(electrification_data == elec_nodata, 0, electrification_data)\n",
        "\n",
        "    # Calculate total population, total electrified population, and total unelectrified population\n",
        "    total_population = np.sum(population_data)\n",
        "    total_electrified_population = np.sum(population_data * electrification_data)\n",
        "    total_unelectrified_population = total_population - total_electrified_population\n",
        "\n",
        "    # Calculate electrification rate\n",
        "    electrification_rate = total_electrified_population / total_population if total_population > 0 else 0\n",
        "\n",
        "    return total_electrified_population, total_unelectrified_population, electrification_rate\n",
        "\n",
        "# Define the radii in kilometers\n",
        "radii_km = [5, 10, 20, 30, 50, 100]\n",
        "\n",
        "# Get the total number of agglomerations\n",
        "total_agglomerations = len(urban_extents)\n",
        "print(f'Total agglomerations to process: {total_agglomerations}')\n",
        "\n",
        "# Loop through each agglomeration\n",
        "for index, row in urban_extents.iterrows():\n",
        "    centroid = row['centroid']\n",
        "    agglos_name = row['agglosName']\n",
        "    iso3 = row['ISO3']\n",
        "    pop2015 = row['Pop2015']\n",
        "    longitude = row['Longitude']\n",
        "    latitude = row['Latitude']\n",
        "\n",
        "    print(f'Processing {agglos_name} ({index + 1}/{total_agglomerations})')\n",
        "\n",
        "    for radius in radii_km:\n",
        "        total_electrified_population, total_unelectrified_population, electrification_rate = calculate_population_stats(centroid, radius)\n",
        "\n",
        "        # Create a DataFrame for the current result\n",
        "        current_result = pd.DataFrame({\n",
        "            'agglosName': [agglos_name],\n",
        "            'ISO3': [iso3],\n",
        "            'Pop2015': [pop2015],\n",
        "            'Longitude': [longitude],\n",
        "            'Latitude': [latitude],\n",
        "            'Radius_Kilometers': [radius],\n",
        "            'Total_Electrified_Population': [total_electrified_population],\n",
        "            'Total_Unelectrified_Population': [total_unelectrified_population],\n",
        "            'Electrification_Rate': [electrification_rate]\n",
        "        })\n",
        "\n",
        "        # Concatenate the current result to the results DataFrame\n",
        "        results = pd.concat([results, current_result], ignore_index=True)\n",
        "\n",
        "print('Processing complete. Saving results to CSV.')\n",
        "\n",
        "# Save the results to a CSV file\n",
        "results.to_csv(os.path.join(fp, 'Outputs/all_agglomerations_unelectrified_population.csv'), index=False)\n",
        "\n",
        "print('Results saved to CSV.')"
      ],
      "metadata": {
        "id": "-ditfQlCAsJK"
      },
      "id": "-ditfQlCAsJK",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Hotspot analysis"
      ],
      "metadata": {
        "id": "NtdLtOccSRMK"
      },
      "id": "NtdLtOccSRMK"
    },
    {
      "cell_type": "code",
      "source": [
        "results_path = os.path.join(fp, 'Outputs/all_agglomerations_unelectrified_population.csv')\n",
        "output_shapefile_path = os.path.join(fp, 'Outputs/unelectrified_population_points.shp')\n",
        "\n",
        "# Load the data\n",
        "if os.path.exists(results_path):\n",
        "    results = pd.read_csv(results_path)\n",
        "\n",
        "# Exclude specified ISO3 codes\n",
        "exclude_iso3 = ['EGY', 'MAR', 'TUN', 'LBY', 'DZA']\n",
        "results = results[~results['ISO3'].isin(exclude_iso3)]\n",
        "\n",
        "# Pivot the data to make Radius_Kilometers as columns\n",
        "pivoted_df = results.pivot_table(index=['agglosName', 'ISO3', 'Pop2015', 'Longitude', 'Latitude'],\n",
        "                                 columns='Radius_Kilometers',\n",
        "                                 values=['Total_Electrified_Population', 'Total_Unelectrified_Population', 'Electrification_Rate'])\n",
        "\n",
        "# Flatten the columns MultiIndex\n",
        "pivoted_df.columns = ['{}_{}'.format(col[1], col[0]) for col in pivoted_df.columns.values]\n",
        "\n",
        "# Shorten field names to avoid truncation in shapefile\n",
        "pivoted_df.columns = pivoted_df.columns.str.replace('Total_Electrified_Population', 'Tot_Elec_Pop')\n",
        "pivoted_df.columns = pivoted_df.columns.str.replace('Total_Unelectrified_Population', 'Tot_Unelec_Pop')\n",
        "pivoted_df.columns = pivoted_df.columns.str.replace('Electrification_Rate', 'Elec_Rate')\n",
        "pivoted_df.columns = pivoted_df.columns.str.replace('total_population', 'Tot_Pop')\n",
        "\n",
        "# Reset index to convert the pivoted DataFrame into a regular DataFrame\n",
        "pivoted_df.reset_index(inplace=True)\n",
        "\n",
        "# Calculate total population for each radius\n",
        "for radius in [5, 10, 20, 30, 50]:\n",
        "    pivoted_df[f'{radius}_Tot_Pop'] = pivoted_df[f'{radius}_Tot_Elec_Pop'] + pivoted_df[f'{radius}_Tot_Unelec_Pop']\n",
        "\n",
        "# Convert DataFrame to GeoDataFrame\n",
        "pivoted_df['geometry'] = pivoted_df.apply(lambda row: Point(row['Longitude'], row['Latitude']), axis=1)\n",
        "results_gdf = gpd.GeoDataFrame(pivoted_df, geometry='geometry')\n",
        "\n",
        "# Export to shapefile\n",
        "results_gdf.to_file(output_shapefile_path, driver='ESRI Shapefile')\n",
        "\n",
        "# Print a message indicating the file has been saved\n",
        "print(f'Shapefile has been exported to {output_shapefile_path}')"
      ],
      "metadata": {
        "id": "ngzMIC6BHg36"
      },
      "id": "ngzMIC6BHg36",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "C7dD454h8oKo",
      "metadata": {
        "id": "C7dD454h8oKo"
      },
      "source": [
        "# **Post-processing**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b34898eb",
      "metadata": {
        "id": "b34898eb"
      },
      "outputs": [],
      "source": [
        "cols = list(output_df.columns)\n",
        "\n",
        "output_df[['Country']+cols[1::9]].to_csv('pop_per_urca.csv')\n",
        "output_df[['Country']+cols[2::9]].to_csv('elec_pop_per_urca.csv')\n",
        "output_df[['Country']+cols[3::9]].to_csv('unelec_pop_per_urca.csv')\n",
        "output_df[['Country']+cols[4::9]].to_csv('urban_pop_per_urca.csv')\n",
        "output_df[['Country']+cols[5::9]].to_csv('rural_pop_per_urca.csv')\n",
        "output_df[['Country']+cols[6::9]].to_csv('urban_elec_pop_per_urca.csv')\n",
        "output_df[['Country']+cols[7::9]].to_csv('rural_elec_pop_per_urca.csv')\n",
        "output_df[['Country']+cols[8::9]].to_csv('urban_unelec_pop_per_urca.csv')\n",
        "output_df[['Country']+cols[9::9]].to_csv('rural_unelec_pop_per_urca.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5b350a4a",
      "metadata": {
        "id": "5b350a4a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "outputId": "4afcb68d-99d2-480d-b65f-dc4ac35ca52f"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'output_df' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-7-d844613f6538>\u001b[0m in \u001b[0;36m<cell line: 13>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcntry\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcountries\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m     \u001b[0;31m# percent urban unelec\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m     new_row_urbn = pd.DataFrame([[cntry] + [output_df[f'{cat}_Urban_Unelec_Pop']\n\u001b[0m\u001b[1;32m     16\u001b[0m                                             \u001b[0;34m[\u001b[0m\u001b[0moutput_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Country'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mcntry\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m                                             for cat in range(1, 31)]],\n",
            "\u001b[0;32m<ipython-input-7-d844613f6538>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcntry\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcountries\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m     \u001b[0;31m# percent urban unelec\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m     new_row_urbn = pd.DataFrame([[cntry] + [output_df[f'{cat}_Urban_Unelec_Pop']\n\u001b[0m\u001b[1;32m     16\u001b[0m                                             \u001b[0;34m[\u001b[0m\u001b[0moutput_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Country'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mcntry\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m                                             for cat in range(1, 31)]],\n",
            "\u001b[0;31mNameError\u001b[0m: name 'output_df' is not defined"
          ]
        }
      ],
      "source": [
        "pcnt_urbn_unelec_df = pd.DataFrame(columns=['Country'] + [f'{cat}_Urban_Unelec_Pop' for cat in range(1, 31)])\n",
        "pcnt_rurl_unelec_df = pd.DataFrame(columns=['Country'] + [f'{cat}_Rural_Unelec_Pop' for cat in range(1, 31)])\n",
        "pcnt_totl_unelec_df = pd.DataFrame(columns=['Country'] + [f'{cat}_Total_Unelec_Pop' for cat in range(1, 31)])\n",
        "\n",
        "cumu_urbn_unelec_df = pd.DataFrame(columns=['Country'] + list(range(1, 31)))\n",
        "cumu_rurl_unelec_df = pd.DataFrame(columns=['Country'] + list(range(1, 31)))\n",
        "cumu_totl_unelec_df = pd.DataFrame(columns=['Country'] + list(range(1, 31)))\n",
        "\n",
        "totl_urbn_unelec_df = pd.DataFrame(columns=['Country', 'Total_Urban_Unelec'])\n",
        "totl_rurl_unelec_df = pd.DataFrame(columns=['Country', 'Total_Rural_Unelec'])\n",
        "totl_unelec_df = pd.DataFrame(columns=['Country', 'Total_Unelec'])\n",
        "\n",
        "for cntry in countries:\n",
        "    # percent urban unelec\n",
        "    new_row_urbn = pd.DataFrame([[cntry] + [output_df[f'{cat}_Urban_Unelec_Pop']\n",
        "                                            [output_df['Country'] == cntry].values[0]\n",
        "                                            for cat in range(1, 31)]],\n",
        "                                columns=['Country'] + [f'{cat}_Urban_Unelec_Pop' for cat in range(1, 31)])\n",
        "    pcnt_urbn_unelec_df = pd.concat([pcnt_urbn_unelec_df, new_row_urbn], ignore_index=True)\n",
        "\n",
        "    # percent rural unelec\n",
        "    new_row_rurl = pd.DataFrame([[cntry] + [output_df[f'{cat}_Rural_Unelec_Pop']\n",
        "                                            [output_df['Country'] == cntry].values[0]\n",
        "                                            for cat in range(1, 31)]],\n",
        "                                columns=['Country'] + [f'{cat}_Rural_Unelec_Pop' for cat in range(1, 31)])\n",
        "    pcnt_rurl_unelec_df = pd.concat([pcnt_rurl_unelec_df, new_row_rurl], ignore_index=True)\n",
        "\n",
        "    # percent total unelec\n",
        "    new_row_totl = pd.DataFrame([[cntry] + [output_df[f'{cat}_Urban_Unelec_Pop']\n",
        "                                            [output_df['Country'] == cntry].values[0]\n",
        "                                            + output_df[f'{cat}_Rural_Unelec_Pop']\n",
        "                                            [output_df['Country'] == cntry].values[0]\n",
        "                                            for cat in range(1, 31)]],\n",
        "                                columns=['Country'] + [f'{cat}_Total_Unelec_Pop' for cat in range(1, 31)])\n",
        "    pcnt_totl_unelec_df = pd.concat([pcnt_totl_unelec_df, new_row_totl], ignore_index=True)\n",
        "\n",
        "    # total urban unelec\n",
        "    totl_urbn_unelec_df = pd.concat([totl_urbn_unelec_df, pd.DataFrame(\n",
        "        [[cntry, pcnt_urbn_unelec_df[pcnt_urbn_unelec_df.columns[1:]].sum(axis=1).values[0]]],\n",
        "        columns=totl_urbn_unelec_df.columns)], ignore_index=True)\n",
        "\n",
        "    # total rural unelec\n",
        "    totl_rurl_unelec_df = pd.concat([totl_rurl_unelec_df, pd.DataFrame([[cntry, sum(\n",
        "        pcnt_rurl_unelec_df[pcnt_rurl_unelec_df['Country'] == cntry].iloc[0, 1:])]],\n",
        "        columns=totl_rurl_unelec_df.columns)], ignore_index=True)\n",
        "\n",
        "    # total unelec\n",
        "    totl_unelec_df = pd.concat([totl_unelec_df, pd.DataFrame([[cntry, sum(\n",
        "        pcnt_totl_unelec_df[pcnt_totl_unelec_df['Country'] == cntry].iloc[0, 1:])]],\n",
        "        columns=totl_unelec_df.columns)], ignore_index=True)\n",
        "\n",
        "    # convert percentage-df values to percentages by dividing by total-df values\n",
        "    pcnt_urbn_unelec_df.loc[pcnt_urbn_unelec_df['Country'] == cntry,\n",
        "                            pcnt_urbn_unelec_df.columns[1:]] /= totl_urbn_unelec_df[\n",
        "        totl_urbn_unelec_df['Country'] == cntry]['Total_Urban_Unelec'].values[0]\n",
        "\n",
        "    pcnt_rurl_unelec_df.loc[pcnt_rurl_unelec_df['Country'] == cntry,\n",
        "                            pcnt_rurl_unelec_df.columns[1:]] /= totl_rurl_unelec_df[\n",
        "        totl_rurl_unelec_df['Country'] == cntry]['Total_Rural_Unelec'].values[0]\n",
        "\n",
        "    pcnt_totl_unelec_df.loc[pcnt_totl_unelec_df['Country'] == cntry,\n",
        "                            pcnt_totl_unelec_df.columns[1:]] /= totl_unelec_df[\n",
        "        totl_unelec_df['Country'] == cntry]['Total_Unelec'].values[0]\n",
        "\n",
        "    # cumulative urban unelec\n",
        "    cumu_urbn_unelec_df = pd.concat([cumu_urbn_unelec_df, pd.DataFrame(\n",
        "        [[cntry] + [round(pcnt_urbn_unelec_df[pcnt_urbn_unelec_df.columns[1:cat + 1]]\n",
        "                          [pcnt_urbn_unelec_df['Country'] == cntry].values.sum(), 4)\n",
        "                    for cat in range(1, 31)]], columns=cumu_urbn_unelec_df.columns)], ignore_index=True)\n",
        "\n",
        "    # cumulative rural unelec\n",
        "    cumu_rurl_unelec_df = pd.concat([cumu_rurl_unelec_df, pd.DataFrame(\n",
        "        [[cntry] + [round(pcnt_rurl_unelec_df[pcnt_rurl_unelec_df.columns[1:cat + 1]]\n",
        "                          [pcnt_rurl_unelec_df['Country'] == cntry].values.sum(), 4)\n",
        "                    for cat in range(1, 31)]], columns=cumu_rurl_unelec_df.columns)], ignore_index=True)\n",
        "\n",
        "    # cumulative total unelec\n",
        "    cumu_totl_unelec_df = pd.concat([cumu_totl_unelec_df, pd.DataFrame(\n",
        "        [[cntry] + [round(pcnt_totl_unelec_df[pcnt_totl_unelec_df.columns[1:cat + 1]]\n",
        "                          [pcnt_totl_unelec_df['Country'] == cntry].values.sum(), 4)\n",
        "                    for cat in range(1, 31)]], columns=cumu_totl_unelec_df.columns)], ignore_index=True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the file path and load the CSV file\n",
        "df_path = os.path.join(path, 'Outputs/rural_urban_elec_unelec_output.csv')\n",
        "\n",
        "if os.path.exists(df_path):\n",
        "    df = pd.read_csv(df_path)\n",
        "\n",
        "# List of countries of interest\n",
        "countries_of_interest = ['DRC', 'Ethiopia', 'Tanzania', 'Nigeria', 'Kenya', 'Uganda']\n",
        "\n",
        "# Initialize an empty DataFrame for the output\n",
        "output_df = pd.DataFrame(columns=['Geography', 'Rural_Elec_Rate', 'Rural_Unelec_Pop', 'Urban_Elec_Rate', 'Urban_Unelec_Pop', 'Total_Unelec_Pop', 'Total_Elec_Rate'])\n",
        "\n",
        "# Function to calculate the required values\n",
        "def calculate_values(df, country):\n",
        "    rural_total_pop = df.loc[df['Country'] == country, 'Rural_Total_Pop'].values[0]\n",
        "    rural_elec_pop = df.loc[df['Country'] == country, 'Rural_Elec_Pop'].values[0]\n",
        "    urban_total_pop = df.loc[df['Country'] == country, 'Urban_Total_Pop'].values[0]\n",
        "    urban_elec_pop = df.loc[df['Country'] == country, 'Urban_Elec_Pop'].values[0]\n",
        "\n",
        "    rural_elec_rate = round((rural_elec_pop / rural_total_pop) * 100, 2)\n",
        "    rural_unelec_pop = round((rural_total_pop - rural_elec_pop) / 1e6, 2)\n",
        "    urban_elec_rate = round((urban_elec_pop / urban_total_pop) * 100, 2)\n",
        "    urban_unelec_pop = round((urban_total_pop - urban_elec_pop) / 1e6, 2)\n",
        "\n",
        "    total_unelec_pop = round((rural_total_pop - rural_elec_pop + urban_total_pop - urban_elec_pop) / 1e6, 2)\n",
        "    total_elec_rate = round(((rural_elec_pop + urban_elec_pop) / (rural_total_pop + urban_total_pop)) * 100, 2)\n",
        "\n",
        "    return rural_elec_rate, rural_unelec_pop, urban_elec_rate, urban_unelec_pop, total_unelec_pop, total_elec_rate\n",
        "\n",
        "# Calculate the values for each country of interest\n",
        "for country in countries_of_interest:\n",
        "    rural_elec_rate, rural_unelec_pop, urban_elec_rate, urban_unelec_pop, total_unelec_pop, total_elec_rate = calculate_values(df, country)\n",
        "\n",
        "    new_row = pd.DataFrame([{\n",
        "        'Geography': country,\n",
        "        'Rural_Elec_Rate': rural_elec_rate,\n",
        "        'Rural_Unelec_Pop': rural_unelec_pop,\n",
        "        'Urban_Elec_Rate': urban_elec_rate,\n",
        "        'Urban_Unelec_Pop': urban_unelec_pop,\n",
        "        'Total_Unelec_Pop': total_unelec_pop,\n",
        "        'Total_Elec_Rate': total_elec_rate\n",
        "    }])\n",
        "\n",
        "    output_df = pd.concat([output_df, new_row], ignore_index=True)\n",
        "\n",
        "# Calculate the totals for SSA\n",
        "rural_total_pop = df['Rural_Total_Pop'].sum()\n",
        "rural_elec_pop = df['Rural_Elec_Pop'].sum()\n",
        "urban_total_pop = df['Urban_Total_Pop'].sum()\n",
        "urban_elec_pop = df['Urban_Elec_Pop'].sum()\n",
        "\n",
        "rural_elec_rate = round((rural_elec_pop / rural_total_pop) * 100, 2)\n",
        "rural_unelec_pop = round((rural_total_pop - rural_elec_pop) / 1e6, 2)\n",
        "urban_elec_rate = round((urban_elec_pop / urban_total_pop) * 100, 2)\n",
        "urban_unelec_pop = round((urban_total_pop - urban_elec_pop) / 1e6, 2)\n",
        "total_unelec_pop = round((rural_total_pop - rural_elec_pop + urban_total_pop - urban_elec_pop) / 1e6, 2)\n",
        "total_elec_rate = round(((rural_elec_pop + urban_elec_pop) / (rural_total_pop + urban_total_pop)) * 100, 2)\n",
        "\n",
        "ssa_row = pd.DataFrame([{\n",
        "    'Geography': 'SSA',\n",
        "    'Rural_Elec_Rate': rural_elec_rate,\n",
        "    'Rural_Unelec_Pop': rural_unelec_pop,\n",
        "    'Urban_Elec_Rate': urban_elec_rate,\n",
        "    'Urban_Unelec_Pop': urban_unelec_pop,\n",
        "    'Total_Unelec_Pop': total_unelec_pop,\n",
        "    'Total_Elec_Rate': total_elec_rate\n",
        "}])\n",
        "\n",
        "output_df = pd.concat([output_df, ssa_row], ignore_index=True)\n",
        "\n",
        "# Print the output DataFrame\n",
        "display(output_df)"
      ],
      "metadata": {
        "id": "HDIYLtEwkJ-0"
      },
      "id": "HDIYLtEwkJ-0",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "c1b4a09b",
      "metadata": {
        "id": "c1b4a09b"
      },
      "source": [
        "# Visualizations"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7SwmCL5GOeGY",
      "metadata": {
        "id": "7SwmCL5GOeGY"
      },
      "source": [
        "## Set styling parameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8f0bea94",
      "metadata": {
        "id": "8f0bea94",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "4735f26a-0c82-4ae4-db74-b2a2fd33260b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<style>div.output_scroll { height: 44em; }</style>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "country_labels = ['DRC',\n",
        "                  'Ethiopia',\n",
        "                  'Tanzania',\n",
        "                  'Nigeria',\n",
        "                  'Kenya',\n",
        "                  'Uganda'\n",
        "                  ]\n",
        "\n",
        "country_codes = [countries.index(cntry) for cntry in country_labels]\n",
        "labels = np.arange(1,31)\n",
        "\n",
        "#Colors array\n",
        "col = ['#002626', '#229665', '#F49D37', '#931621', '#0CA2D4', '#EC5409']\n",
        "\n",
        "#Specify font sizes\n",
        "SMALL_SIZE = 14\n",
        "MEDIUM_SIZE = 16\n",
        "BIGGER_SIZE = 26\n",
        "\n",
        "plt.rc('font', size=SMALL_SIZE)          # controls default text sizes\n",
        "plt.rc('axes', titlesize=SMALL_SIZE)     # fontsize of the axes title\n",
        "plt.rc('axes', labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels\n",
        "plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
        "plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
        "plt.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize\n",
        "plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure title\n",
        "\n",
        "#Make output window larger\n",
        "from IPython.core.display import display, HTML\n",
        "display(HTML(\"<style>div.output_scroll { height: 44em; }</style>\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "wQ3TBj1tRlrn",
      "metadata": {
        "id": "wQ3TBj1tRlrn"
      },
      "outputs": [],
      "source": [
        "x_axis_labels = ['>5 million',\n",
        "                 '1-5 million',\n",
        "                 '500,000-1 million',\n",
        "                 '250,000-500,000',\n",
        "                 '100,000-250,000',\n",
        "                 '50,000-100,000',\n",
        "                 '20,000-50,000',\n",
        "                 '>5 million',\n",
        "                 '1-5 million',\n",
        "                 '500,000-1 million',\n",
        "                 '250,000-500,000',\n",
        "                 '100,000-250,000',\n",
        "                 '50,000-100,000',\n",
        "                 '20,000-50,000',\n",
        "                 '>5 million',\n",
        "                 '1-5 million',\n",
        "                 '500,000-1 million',\n",
        "                 '250,000-500,000',\n",
        "                 '100,000-250,000',\n",
        "                 '50,000-100,000',\n",
        "                 '20,000-50,000',\n",
        "                 '>5 million',\n",
        "                 '1-5 million',\n",
        "                 '500,000-1 million',\n",
        "                 '250,000-500,000',\n",
        "                 '100,000-250,000',\n",
        "                 '50,000-100,000',\n",
        "                 '20,000-50,000',\n",
        "                 'Dispersed town',\n",
        "                 'Hinterland']"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_axis_labels_alt = ['Within urban core',\n",
        "                     '< 1 hour',\n",
        "                     '1-2 hours',\n",
        "                     '2-3 hours',\n",
        "                     'Within urban core',\n",
        "                     '< 1 hour',\n",
        "                     '1-2 hours',\n",
        "                     '2-3 hours',\n",
        "                     'Within urban core',\n",
        "                     '< 1 hour',\n",
        "                     '1-2 hours',\n",
        "                     '2-3 hours',\n",
        "                     'Within urban core',\n",
        "                     '< 1 hour',\n",
        "                     '1-2 hours',\n",
        "                     '2-3 hours',\n",
        "                     'Within urban core',\n",
        "                     '< 1 hour',\n",
        "                     '1-2 hours',\n",
        "                     '2-3 hours',\n",
        "                     'Within urban core',\n",
        "                     '< 1 hour',\n",
        "                     '1-2 hours',\n",
        "                     '2-3 hours',\n",
        "                     'Within urban core',\n",
        "                     '< 1 hour',\n",
        "                     '1-2 hours',\n",
        "                     '2-3 hours',\n",
        "                     '3+ hours',\n",
        "                     '3+ hours']"
      ],
      "metadata": {
        "id": "0EleNS5OAEBX"
      },
      "id": "0EleNS5OAEBX",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the colors\n",
        "colors = ['#002626', '#229665', '#F49D37', '#931621', '#0CA2D4', '#EC5409', '#CCCCCC']"
      ],
      "metadata": {
        "id": "3dzDyySQb9un"
      },
      "id": "3dzDyySQb9un",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Distribution of the unelectrified by rural/urban binary"
      ],
      "metadata": {
        "id": "j-13oEICkCDy"
      },
      "id": "j-13oEICkCDy"
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the file path and load the CSV file\n",
        "df_path = os.path.join(path, 'Outputs/rural_urban_elec_unelec_output.csv')\n",
        "\n",
        "if os.path.exists(df_path):\n",
        "    df = pd.read_csv(df_path)\n",
        "\n",
        "# Filter the DataFrame to include only the specified countries\n",
        "df_filtered = df[df['Country'].isin(countries)]\n",
        "\n",
        "# Calculate the required values\n",
        "df_filtered['Total_Unelec_Pop'] = df_filtered['Rural_Unelec_Pop'] + df_filtered['Urban_Unelec_Pop']\n",
        "df_filtered = df_filtered.sort_values(by='Total_Unelec_Pop', ascending=True)  # Sort in ascending order\n",
        "\n",
        "# Extract the values for plotting\n",
        "rural_unelec_pop = df_filtered['Rural_Unelec_Pop'] / 1e6  # Convert to millions\n",
        "urban_unelec_pop = df_filtered['Urban_Unelec_Pop'] / 1e6  # Convert to millions\n",
        "countries_sorted = df_filtered['Country']\n",
        "\n",
        "# Define the colors\n",
        "col = ['#002626', '#F49D37', '#931621', '#0CA2D4', '#EC5409']\n",
        "\n",
        "# Create the horizontal bar chart\n",
        "fig, ax = plt.subplots(figsize=(5, 7))\n",
        "\n",
        "# Define the bar positions and width\n",
        "bar_width = 0.4\n",
        "indices = np.arange(len(countries_sorted))\n",
        "\n",
        "# Plot the rural and urban unelectrified populations as stacked bars\n",
        "p1 = ax.barh(indices, rural_unelec_pop, bar_width, label='Rural', color=col[0])\n",
        "p2 = ax.barh(indices, urban_unelec_pop, bar_width, left=rural_unelec_pop, label='Urban', color=col[1])\n",
        "\n",
        "# Set the y-ticks and y-tick labels\n",
        "ax.set_yticks(indices)\n",
        "ax.set_yticklabels(countries_sorted, fontname='sans-serif', fontsize=10)\n",
        "\n",
        "# Set labels\n",
        "ax.set_xlabel('Unelectrified population (millions)', fontname='sans-serif', fontsize=10)\n",
        "\n",
        "# Remove the frame from the legend and move it to the bottom right\n",
        "legend = ax.legend(fontsize=10, frameon=False, loc='lower right')\n",
        "legend.get_texts()[0].set_text('Rural')\n",
        "legend.get_texts()[1].set_text('Urban')\n",
        "\n",
        "# Adjust the ylim to remove the gaps\n",
        "ax.set_ylim(-0.5, len(countries_sorted) - 0.5)\n",
        "\n",
        "# Adjust the x-tick label fontsize\n",
        "ax.set_xticks(np.arange(0, max(rural_unelec_pop + urban_unelec_pop) + 25, 25))\n",
        "\n",
        "\n",
        "# Move the x-axis to the top\n",
        "ax.xaxis.set_ticks_position('top')\n",
        "ax.xaxis.set_label_position('top')\n",
        "\n",
        "plt.xticks(fontsize=10)  # Change the value of fontsize to the desired size\n",
        "\n",
        "# Display the plot\n",
        "plt.tight_layout()\n",
        "\n",
        "# Save the plot\n",
        "path_save = fp + 'Graphics/rural_urban_unelec.png'\n",
        "plt.savefig(path_save, dpi=500, bbox_inches='tight')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 694
        },
        "id": "_UtgNzOhAfNl",
        "outputId": "e345b442-0bbc-4f1b-dad6-cff02d813246"
      },
      "id": "_UtgNzOhAfNl",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(indices, rural_unelec_pop)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BOAn1zCv9QVm",
        "outputId": "4b290252-6aab-4a5e-f2b4-ac6ec404a6e1"
      },
      "id": "BOAn1zCv9QVm",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n",
            " 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42] 11    0.619513\n",
            "13    0.808081\n",
            "15    0.982478\n",
            "16    0.942645\n",
            "2     1.103994\n",
            "28    1.114331\n",
            "19    1.240936\n",
            "21    1.603803\n",
            "9     1.692254\n",
            "26    2.410558\n",
            "22    2.679932\n",
            "6       3.4501\n",
            "39    3.669481\n",
            "12    3.555581\n",
            "33    4.131228\n",
            "1     6.409863\n",
            "34    7.914819\n",
            "32    8.181691\n",
            "35    8.162666\n",
            "18    8.407639\n",
            "4     8.096175\n",
            "31    5.598191\n",
            "41    9.521441\n",
            "42    9.875751\n",
            "17    10.44057\n",
            "36   10.179568\n",
            "10   11.645943\n",
            "7      10.1501\n",
            "25   12.562849\n",
            "0    12.414871\n",
            "24   12.765681\n",
            "3    13.374522\n",
            "5     12.60684\n",
            "29   16.250947\n",
            "27   18.665102\n",
            "23    21.59591\n",
            "37    25.64339\n",
            "20   17.641662\n",
            "40   24.597674\n",
            "38    34.44576\n",
            "8    54.981584\n",
            "14   65.246452\n",
            "30   95.793264\n",
            "Name: Rural_Unelec_Pop, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Cumulative electrification rate across URCA categories, country-level"
      ],
      "metadata": {
        "id": "7S50ABCN8jiL"
      },
      "id": "7S50ABCN8jiL"
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the file path and load the CSV file\n",
        "df_path = os.path.join(path, 'Outputs/rural_urban_elec_unelec_URCA_output.csv')\n",
        "\n",
        "if os.path.exists(df_path):\n",
        "    df = pd.read_csv(df_path)"
      ],
      "metadata": {
        "id": "lenF6srpCpUJ"
      },
      "id": "lenF6srpCpUJ",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create arrays to hold cumulative data\n",
        "cumulative_data = np.zeros((len(countries), 30))\n",
        "\n",
        "# Extract cumulative unelectrified percent data for each country\n",
        "for i, country in enumerate(countries):\n",
        "    cumulative_data[i, :] = calculate_figure(df, 'cumulative_unelectrified_percent', list(range(1, 31)), country=country)\n",
        "\n",
        "# Plotting\n",
        "fig, ax = plt.subplots(figsize=(15, 8))\n",
        "labels = np.arange(1, 31)\n",
        "\n",
        "# Plot all countries in gray\n",
        "for l in range(len(countries)):\n",
        "    ax.plot(labels, cumulative_data[l, :], color='#D8D4D5')\n",
        "    print(countries[l])\n",
        "    display(cumulative_data[l,:])\n",
        "\n",
        "# Plot specific countries with distinct colors\n",
        "for p in range(len(country_labels)):\n",
        "    ax.plot(labels, cumulative_data[country_codes[p], :], label=country_labels[p], color=colors[p])\n",
        "\n",
        "# Configure the axes\n",
        "ax.set_xticks(labels)\n",
        "ax.set_xticklabels(labels, fontsize=14)\n",
        "ax.set_xlim(0.5, 30.5)\n",
        "ax.set_ylim(0, 1)\n",
        "ax.set_xlabel('URCA category', fontsize=14)\n",
        "ax.set_ylabel('Cumulative percent of unelectrified population', fontsize=14)\n",
        "\n",
        "# Format y-axis with percentage\n",
        "vals = ax.get_yticks()\n",
        "ax.set_yticklabels(['{:,.0%}'.format(x) for x in vals])\n",
        "\n",
        "# Move legend to the top right\n",
        "ax.legend(fontsize=14, frameon=False, loc='upper right', bbox_to_anchor=(1.15, 1))\n",
        "\n",
        "# Add vertical lines demarcating 'tipping points' with specified linewidth\n",
        "plt.axvline(7.5, color='black', zorder=0, linewidth=0.5)\n",
        "plt.axvline(14.5, color='black', zorder=0, linewidth=0.5)\n",
        "plt.axvline(21.5, color='black', zorder=0, linewidth=0.5)\n",
        "plt.axvline(28.5, color='black', zorder=0, linewidth=0.5)\n",
        "\n",
        "# Add the bottom labels closer to the axis\n",
        "for i, label in enumerate(x_axis_labels):\n",
        "    ax.text(i + 1, -0.02, label, ha='center', va='top', rotation=90, transform=ax.get_xaxis_transform())\n",
        "\n",
        "# Swap URCA category labels to the top\n",
        "ax.xaxis.tick_top()\n",
        "ax.xaxis.set_label_position('top')\n",
        "\n",
        "# Add center-justified text below lower axis labels\n",
        "top_labels = ['Within urban core', '<1 hour to city', '1-2 hours to city', '2-3 hours to city']\n",
        "top_positions = [4, 11, 18, 25]\n",
        "for i, label in enumerate(top_labels):\n",
        "    plt.text(top_positions[i], -0.34, label, ha='center', va='center', fontsize=14, transform=ax.get_xaxis_transform())\n",
        "\n",
        "# Enable tick marks on the bottom x-axis\n",
        "ax.tick_params(axis='x', which='both', bottom=True, top=True)\n",
        "\n",
        "# Add label below the bottom x-axis\n",
        "plt.text(15, -.41, 'Distance from city of reference based on population', ha='center', va='center', fontsize=14)\n",
        "\n",
        "# Save the plot\n",
        "path_save = fp + 'Graphics/Cumulative_Unelectrified_Line_Graph.png'\n",
        "plt.savefig(path_save, dpi=500, bbox_inches='tight')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "xiw9hpv9CriF",
        "outputId": "099853ac-7d27-4901-d47e-fb5663f811d3"
      },
      "id": "xiw9hpv9CriF",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Angola\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "       0.02731256, 0.03745376, 0.06357931, 0.06357931, 0.11147824,\n",
              "       0.2056522 , 0.30604393, 0.37011325, 0.38942907, 0.39539353,\n",
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Benin\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "       1.73059991e-01, 1.91502114e-01, 2.25293345e-01, 4.46899861e-01,\n",
              "       7.12895211e-01, 7.26785287e-01, 7.26818482e-01, 7.32442756e-01,\n",
              "       7.32983570e-01, 7.67029252e-01, 8.51716189e-01, 9.39879813e-01,\n",
              "       9.69148203e-01, 9.69148203e-01, 9.69148203e-01, 9.69148203e-01,\n",
              "       9.71919237e-01, 9.80153126e-01, 9.86134856e-01, 9.91279719e-01,\n",
              "       9.91279719e-01, 1.00000000e+00])"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Botswana\n"
          ]
        },
        {
          "output_type": "display_data",
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              "       5.80700101e-04, 5.80700101e-04, 5.80700101e-04, 1.69404080e-01,\n",
              "       3.32196695e-01, 3.68786237e-01, 3.68786237e-01, 3.68786237e-01,\n",
              "       3.68786237e-01, 3.68786237e-01, 4.59323815e-01, 6.07107914e-01,\n",
              "       7.17274243e-01, 7.17274243e-01, 7.17274243e-01, 7.17274243e-01,\n",
              "       7.17274243e-01, 7.45366176e-01, 7.93427489e-01, 8.62663973e-01,\n",
              "       8.62724670e-01, 1.00000000e+00])"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Burkina Faso\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "       6.73500380e-01, 6.74393711e-01, 7.86582345e-01, 9.04017454e-01,\n",
              "       9.49745129e-01, 9.49745129e-01, 9.50872333e-01, 9.53441336e-01,\n",
              "       9.53735317e-01, 9.67325051e-01, 9.81553197e-01, 9.90032600e-01,\n",
              "       9.90131555e-01, 1.00000000e+00])"
            ]
          },
          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Burundi\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "       0.04683978, 0.09660869, 0.09660869, 0.09660869, 0.42615015,\n",
              "       0.4263612 , 0.85284477, 0.98885996, 0.98887293, 0.98887293,\n",
              "       0.98941783, 0.99190645, 0.9919898 , 0.99759626, 0.99834056,\n",
              "       0.99860021, 0.99860021, 0.99860021, 0.99889647, 0.99889647,\n",
              "       0.99913432, 0.9993116 , 0.99938972, 0.99938972, 1.        ])"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cameroon\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "       1.64179113e-02, 2.10537143e-02, 3.24981502e-02, 3.24981502e-02,\n",
              "       1.20813225e-01, 1.97513407e-01, 3.95677394e-01, 5.23398036e-01,\n",
              "       6.02032712e-01, 6.11518165e-01, 6.11518165e-01, 6.77384619e-01,\n",
              "       7.00419183e-01, 7.40061707e-01, 7.99600270e-01, 8.40575755e-01,\n",
              "       8.65106977e-01, 8.65106977e-01, 8.84124241e-01, 8.84820280e-01,\n",
              "       8.89807897e-01, 9.10180955e-01, 9.24520888e-01, 9.33440749e-01,\n",
              "       9.33487037e-01, 1.00000000e+00])"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Central African Republic\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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              "       0.02862396, 0.05677811, 0.05677811, 0.09410546, 0.09410546,\n",
              "       0.09410546, 0.09673535, 0.18215526, 0.35278564, 0.35278564,\n",
              "       0.37122384, 0.37122384, 0.37122669, 0.37660257, 0.48813911,\n",
              "       0.64372266, 0.64372266, 0.65182307, 0.65182307, 0.65189883,\n",
              "       0.65657218, 0.73998638, 0.82988882, 0.83459476, 1.        ])"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Chad\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([0.        , 0.        , 0.        , 0.01705659, 0.07297201,\n",
              "       0.09295019, 0.10695555, 0.10695555, 0.13684506, 0.13684506,\n",
              "       0.21074894, 0.35053757, 0.4304196 , 0.44577155, 0.44577155,\n",
              "       0.45602171, 0.45603444, 0.54383975, 0.64706323, 0.68975913,\n",
              "       0.71575391, 0.71575391, 0.71932255, 0.71939585, 0.74205615,\n",
              "       0.78357471, 0.80336983, 0.81648184, 0.81749368, 1.        ])"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DRC\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([1.33700783e-04, 2.98113155e-02, 3.93989868e-02, 5.97949524e-02,\n",
              "       8.56119626e-02, 1.03587957e-01, 1.21583330e-01, 1.29459007e-01,\n",
              "       1.81118082e-01, 2.68282611e-01, 3.25239488e-01, 4.23164249e-01,\n",
              "       5.07867781e-01, 5.28698659e-01, 5.29895273e-01, 5.36909895e-01,\n",
              "       5.64924429e-01, 6.00360695e-01, 6.64135894e-01, 7.22997301e-01,\n",
              "       7.64049283e-01, 7.64423762e-01, 7.67157135e-01, 7.75289666e-01,\n",
              "       7.90026498e-01, 8.16185358e-01, 8.32937951e-01, 8.54925238e-01,\n",
              "       8.56217828e-01, 1.00000000e+00])"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ROC\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([0.        , 0.        , 0.        , 0.04750894, 0.06331602,\n",
              "       0.06804981, 0.0846385 , 0.08509302, 0.08745274, 0.09455454,\n",
              "       0.10252234, 0.1553288 , 0.16083538, 0.18457054, 0.18581329,\n",
              "       0.19077103, 0.20494373, 0.20993236, 0.28790428, 0.29597798,\n",
              "       0.34913637, 0.35001175, 0.35163675, 0.36105083, 0.36338133,\n",
              "       0.40095382, 0.41130286, 0.46206731, 0.46799642, 1.        ])"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CDI\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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              "       0.00000000e+00, 1.04179905e-04, 3.05035730e-03, 3.05035730e-03,\n",
              "       2.74138008e-02, 5.62150388e-02, 1.10809352e-01, 3.38096553e-01,\n",
              "       5.19703351e-01, 5.53094653e-01, 5.53094653e-01, 5.76819963e-01,\n",
              "       6.12947650e-01, 6.52539804e-01, 7.80314563e-01, 8.75273662e-01,\n",
              "       9.49672094e-01, 9.49672094e-01, 9.51505139e-01, 9.54355221e-01,\n",
              "       9.55767672e-01, 9.63596998e-01, 9.71659697e-01, 9.84366950e-01,\n",
              "       9.84366950e-01, 1.00000000e+00])"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Equatorial Guinea\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "       0.72145237, 0.73206788, 0.79400557, 0.79400557, 1.        ])"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Eritrea\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "       0.32553463, 0.3966663 , 0.41068423, 0.44120403, 0.44120403,\n",
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Swaziland\n"
          ]
        },
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              "       6.39083158e-01, 6.39172530e-01, 6.39172530e-01, 6.40214017e-01,\n",
              "       6.40214017e-01, 6.40214017e-01, 6.82183309e-01, 9.91075865e-01,\n",
              "       9.96369096e-01, 9.96369096e-01, 9.96369096e-01, 9.96369096e-01,\n",
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              "       9.99908244e-01, 1.00000000e+00])"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ethiopia\n"
          ]
        },
        {
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              "       0.73904825, 0.7390806 , 0.76285941, 0.84494894, 0.86846901,\n",
              "       0.8798287 , 0.8798287 , 0.88019425, 0.88031894, 0.88801812,\n",
              "       0.92065959, 0.93102838, 0.93749318, 0.93796199, 1.        ])"
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          "metadata": {}
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        {
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          "text": [
            "Gabon\n"
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        {
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          "text": [
            "Gambia\n"
          ]
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        {
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          "name": "stdout",
          "text": [
            "Ghana\n"
          ]
        },
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              "       7.24339807e-01, 7.36370534e-01, 8.18871439e-01, 9.38718271e-01,\n",
              "       9.64584483e-01, 9.64584483e-01, 9.65755611e-01, 9.66434863e-01,\n",
              "       9.68212688e-01, 9.72431105e-01, 9.83680026e-01, 9.90047284e-01,\n",
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          },
          "metadata": {}
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        {
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          "text": [
            "Guinea\n"
          ]
        },
        {
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              "array([0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
              "       6.09831570e-04, 6.23785343e-03, 1.89582681e-02, 1.89582681e-02,\n",
              "       1.68565973e-01, 1.68565973e-01, 1.68565973e-01, 6.77197545e-01,\n",
              "       7.96595073e-01, 7.96741070e-01, 7.96741070e-01, 8.14610686e-01,\n",
              "       8.14610686e-01, 8.14610686e-01, 9.75732063e-01, 9.86434348e-01,\n",
              "       9.92469614e-01, 9.92469614e-01, 9.92494162e-01, 9.92494162e-01,\n",
              "       9.92494162e-01, 9.97890655e-01, 9.98850881e-01, 9.99009541e-01,\n",
              "       9.99009541e-01, 1.00000000e+00])"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Uganda\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([0.        , 0.        , 0.        , 0.        , 0.00104856,\n",
              "       0.00650331, 0.03666326, 0.03666326, 0.10721916, 0.10774834,\n",
              "       0.1168228 , 0.53673562, 0.76930602, 0.78755861, 0.78755861,\n",
              "       0.80089886, 0.81412257, 0.81878324, 0.91619606, 0.96760649,\n",
              "       0.98585856, 0.98585856, 0.9861118 , 0.98612281, 0.98615337,\n",
              "       0.98736106, 0.98949268, 0.9937928 , 0.99403179, 1.        ])"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Zambia\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([0.00000000e+00, 0.00000000e+00, 5.78199984e-04, 7.66317558e-04,\n",
              "       1.80557997e-03, 6.91603904e-03, 1.99808764e-02, 1.99808764e-02,\n",
              "       4.93882486e-02, 1.03570008e-01, 1.47292574e-01, 3.41482531e-01,\n",
              "       4.83017619e-01, 5.14222673e-01, 5.14222673e-01, 5.32087176e-01,\n",
              "       5.38933773e-01, 5.77624245e-01, 6.75252446e-01, 7.67851630e-01,\n",
              "       8.12128291e-01, 8.12128291e-01, 8.14578025e-01, 8.17174129e-01,\n",
              "       8.27206007e-01, 8.53265830e-01, 8.85789246e-01, 9.07192902e-01,\n",
              "       9.08237396e-01, 1.00000000e+00])"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Zimbabwe\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "array([0.00000000e+00, 8.62230127e-04, 1.08254352e-03, 1.08254352e-03,\n",
              "       7.78736132e-03, 1.12987305e-02, 2.71413750e-02, 2.71413750e-02,\n",
              "       5.31016032e-02, 6.75993599e-02, 7.00361535e-02, 1.05122854e-01,\n",
              "       2.19234634e-01, 3.01490910e-01, 3.01490910e-01, 3.24544535e-01,\n",
              "       3.30584927e-01, 3.31572353e-01, 3.95660842e-01, 5.59287459e-01,\n",
              "       6.69247049e-01, 6.69247049e-01, 6.74787652e-01, 6.79720003e-01,\n",
              "       6.80322080e-01, 7.06981395e-01, 7.95402263e-01, 8.61179456e-01,\n",
              "       8.63078314e-01, 1.00000000e+00])"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-13-1d9b2029599f>:32: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
            "  ax.set_yticklabels(['{:,.0%}'.format(x) for x in vals])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Distribution of the unelectrified by URCA along urban hierarchy"
      ],
      "metadata": {
        "id": "qLTEzsuU73wk"
      },
      "id": "qLTEzsuU73wk"
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the file path and load the CSV file\n",
        "df_path = os.path.join(path, 'Outputs/rural_urban_elec_unelec_URCA_output.csv')\n",
        "\n",
        "if os.path.exists(df_path):\n",
        "    df = pd.read_csv(df_path)\n",
        "\n",
        "# List of countries of interest\n",
        "countries_of_interest = ['DRC', 'Ethiopia', 'Tanzania', 'Nigeria', 'Kenya', 'Uganda']\n",
        "\n",
        "# Define the new URCA categories order\n",
        "new_urca_order = [1, 8, 15, 22, 2, 9, 16, 23, 3, 10, 17, 24, 4, 11, 18, 25, 5, 12, 19, 26, 6, 13, 20, 27, 7, 14, 21, 28, 29, 30]\n",
        "\n",
        "# Define the new x-axis labels\n",
        "x_axis_labels_alt = ['Within urban core', '< 1 hour', '1-2 hours', '2-3 hours', 'Within urban core', '< 1 hour', '1-2 hours', '2-3 hours', 'Within urban core', '< 1 hour', '1-2 hours', '2-3 hours', 'Within urban core', '< 1 hour', '1-2 hours', '2-3 hours', 'Within urban core', '< 1 hour', '1-2 hours', '2-3 hours', 'Within urban core', '< 1 hour', '1-2 hours', '2-3 hours', 'Within urban core', '< 1 hour', '1-2 hours', '2-3 hours', 'Dispersed town', 'Hinterland']\n",
        "\n",
        "# Initialize a DataFrame to store unelectrified populations\n",
        "unelectrified_populations = pd.DataFrame()\n",
        "\n",
        "# Calculate unelectrified populations for each URCA category in the new order\n",
        "for i in new_urca_order:\n",
        "    unelec_pop_col = f\"{i}_Unelec_Pop\"\n",
        "    unelectrified_populations[f\"URCA_{i}\"] = df[unelec_pop_col]\n",
        "\n",
        "# Add the country column for reference\n",
        "unelectrified_populations['Country'] = df['Country']\n",
        "\n",
        "# Separate countries of interest and \"Other\"\n",
        "df_countries_of_interest = unelectrified_populations[unelectrified_populations['Country'].isin(countries_of_interest)]\n",
        "df_other_countries = unelectrified_populations[~unelectrified_populations['Country'].isin(countries_of_interest)]\n",
        "\n",
        "# Aggregate \"Other\" countries\n",
        "df_other_countries_aggregated = df_other_countries.drop(columns=['Country']).sum()\n",
        "df_other_countries_aggregated['Country'] = 'Other'\n",
        "\n",
        "# Combine the data\n",
        "combined_data = pd.concat([df_countries_of_interest, df_other_countries_aggregated.to_frame().T])\n",
        "\n",
        "# Plot the stacked bar chart\n",
        "fig, ax = plt.subplots(figsize=(10, 10\n",
        "                                ))\n",
        "\n",
        "# Define the colors\n",
        "colors = ['#002626', '#229665', '#F49D37', '#931621', '#0CA2D4', '#EC5409', '#CCCCCC']\n",
        "\n",
        "# Define the bar positions and width\n",
        "bar_positions = range(len(new_urca_order))\n",
        "bar_width = 0.5\n",
        "\n",
        "# Plot the stacked bars\n",
        "bottoms = [0] * len(new_urca_order)\n",
        "for i, country in enumerate(combined_data['Country']):\n",
        "    values = combined_data.loc[combined_data['Country'] == country].drop(columns=['Country']).values.flatten()\n",
        "    ax.bar(bar_positions, values, bar_width, bottom=bottoms, label=country, color=colors[i % len(colors)])\n",
        "    bottoms = [bottoms[j] + values[j] for j in range(len(values))]\n",
        "\n",
        "# Add vertical lines\n",
        "for x_pos in [3.5, 7.5, 11.5, 15.5, 19.5, 23.5, 27.5]:\n",
        "    ax.axvline(x=x_pos, color='black', linewidth=0.5)\n",
        "\n",
        "# Set labels\n",
        "ax.set_ylabel('Unelectrified population (millions)', fontsize=11)\n",
        "ax.set_xticks(bar_positions)\n",
        "ax.set_xticklabels(x_axis_labels_alt, rotation=90, fontsize = 10)\n",
        "ax.set_xlim(-0.5, len(new_urca_order) - 0.5)\n",
        "\n",
        "# Format the y-axis labels\n",
        "ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f'{int(x / 1e6)}'))\n",
        "\n",
        "# Move legend to upper left and remove outline\n",
        "legend = ax.legend(loc='upper left', frameon=False, fontsize=10, bbox_to_anchor=(1.05, 1))\n",
        "\n",
        "# Individual labels below x-axis, centered between specified categories\n",
        "group_labels = ['>5\\nmillion', '1-5\\nmillion', '500,000-\\n1 million', '250,000-\\n500,000', '100,000-\\n250,000', '50,000-\\n100,000', '20,000-\\n50,000']\n",
        "label_positions = [1.5, 5.5, 9.5, 13.5, 17.5, 21.5, 25.5]\n",
        "for pos, label in zip(label_positions, group_labels):\n",
        "    ax.text(pos, -11e7, label, ha='center', fontsize=10, color='black', fontname='sans-serif')\n",
        "\n",
        "# Add top x-axis for URCA categories\n",
        "ax_top = ax.twiny()\n",
        "ax_top.set_xlim(ax.get_xlim())\n",
        "ax_top.set_xticks(bar_positions)\n",
        "ax_top.set_xticklabels(new_urca_order, fontsize=10)\n",
        "ax_top.set_xlabel('URCA category', fontsize=12)\n",
        "\n",
        "plt.yticks(fontsize=10)\n",
        "\n",
        "# Save the plot\n",
        "path_save = fp + 'Graphics/urban_hierarchy_dist.png'\n",
        "plt.savefig(path_save, dpi=500, bbox_inches='tight')\n",
        "\n",
        "# Adjust layout\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        },
        "id": "y-9EYCDA7jFL",
        "outputId": "c8c58c10-1103-4e5a-b073-a3a056a30496"
      },
      "id": "y-9EYCDA7jFL",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "display(combined_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "Tad6cMguc1sJ",
        "outputId": "00380d12-4d55-4c94-c680-cfec6dbf4a02"
      },
      "id": "Tad6cMguc1sJ",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   URCA_1   URCA_8 URCA_15 URCA_22   URCA_2    URCA_9  URCA_16 URCA_23  \\\n",
              "8    8949   527143   80093   25065  1986413   3457699   469510  182953   \n",
              "14      0        0       0       0        0   1163974   153388   28420   \n",
              "20      0        0       0       0        0   2068146   684754  174020   \n",
              "30      0  1889103   65981    5524  1788142  27590484  1347070  123276   \n",
              "38      0   290333  183998   16892        0         0     6765     696   \n",
              "40      0        0       0       0        0   2153961   407257    7731   \n",
              "0       0  1338583  540167  281639    85145  15316485  7554171  918639   \n",
              "\n",
              "    URCA_3   URCA_10  ...   URCA_13   URCA_20  URCA_27   URCA_7   URCA_14  \\\n",
              "8   641732   5834187  ...   5669465   3939773  1121302  1204485   1394274   \n",
              "14       0         0  ...   5743525   1828600   806136  1710580    288611   \n",
              "20       0         0  ...   5191712   1053710   171903   131413    786435   \n",
              "30  686108  15097534  ...   6186576    818881   201286  2593887     84671   \n",
              "38    1110   1410285  ...   4675846   3755613   418132   399234   4287787   \n",
              "40       0     16155  ...   7100010   1569480    65075   920736    557223   \n",
              "0   502608  16166156  ...  37758802  21408476  6163738  4716669  13150256   \n",
              "\n",
              "     URCA_21  URCA_28 URCA_29   URCA_30   Country  \n",
              "8    2747734  1471676   86517   9623778       DRC  \n",
              "14    883175   502615   36448   4823230  Ethiopia  \n",
              "20   1456457   553221    2415   1132200     Kenya  \n",
              "30    487804   130666   10349    878949   Nigeria  \n",
              "38   3260821  1160598   31988   1425274  Tanzania  \n",
              "40    557207   131276    7296    182200    Uganda  \n",
              "0   17138615  7685768  455014  35469650     Other  \n",
              "\n",
              "[7 rows x 31 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9364ce7a-60be-4abb-8efd-a2e8797573de\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>URCA_1</th>\n",
              "      <th>URCA_8</th>\n",
              "      <th>URCA_15</th>\n",
              "      <th>URCA_22</th>\n",
              "      <th>URCA_2</th>\n",
              "      <th>URCA_9</th>\n",
              "      <th>URCA_16</th>\n",
              "      <th>URCA_23</th>\n",
              "      <th>URCA_3</th>\n",
              "      <th>URCA_10</th>\n",
              "      <th>...</th>\n",
              "      <th>URCA_13</th>\n",
              "      <th>URCA_20</th>\n",
              "      <th>URCA_27</th>\n",
              "      <th>URCA_7</th>\n",
              "      <th>URCA_14</th>\n",
              "      <th>URCA_21</th>\n",
              "      <th>URCA_28</th>\n",
              "      <th>URCA_29</th>\n",
              "      <th>URCA_30</th>\n",
              "      <th>Country</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8949</td>\n",
              "      <td>527143</td>\n",
              "      <td>80093</td>\n",
              "      <td>25065</td>\n",
              "      <td>1986413</td>\n",
              "      <td>3457699</td>\n",
              "      <td>469510</td>\n",
              "      <td>182953</td>\n",
              "      <td>641732</td>\n",
              "      <td>5834187</td>\n",
              "      <td>...</td>\n",
              "      <td>5669465</td>\n",
              "      <td>3939773</td>\n",
              "      <td>1121302</td>\n",
              "      <td>1204485</td>\n",
              "      <td>1394274</td>\n",
              "      <td>2747734</td>\n",
              "      <td>1471676</td>\n",
              "      <td>86517</td>\n",
              "      <td>9623778</td>\n",
              "      <td>DRC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1163974</td>\n",
              "      <td>153388</td>\n",
              "      <td>28420</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>5743525</td>\n",
              "      <td>1828600</td>\n",
              "      <td>806136</td>\n",
              "      <td>1710580</td>\n",
              "      <td>288611</td>\n",
              "      <td>883175</td>\n",
              "      <td>502615</td>\n",
              "      <td>36448</td>\n",
              "      <td>4823230</td>\n",
              "      <td>Ethiopia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2068146</td>\n",
              "      <td>684754</td>\n",
              "      <td>174020</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>5191712</td>\n",
              "      <td>1053710</td>\n",
              "      <td>171903</td>\n",
              "      <td>131413</td>\n",
              "      <td>786435</td>\n",
              "      <td>1456457</td>\n",
              "      <td>553221</td>\n",
              "      <td>2415</td>\n",
              "      <td>1132200</td>\n",
              "      <td>Kenya</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>0</td>\n",
              "      <td>1889103</td>\n",
              "      <td>65981</td>\n",
              "      <td>5524</td>\n",
              "      <td>1788142</td>\n",
              "      <td>27590484</td>\n",
              "      <td>1347070</td>\n",
              "      <td>123276</td>\n",
              "      <td>686108</td>\n",
              "      <td>15097534</td>\n",
              "      <td>...</td>\n",
              "      <td>6186576</td>\n",
              "      <td>818881</td>\n",
              "      <td>201286</td>\n",
              "      <td>2593887</td>\n",
              "      <td>84671</td>\n",
              "      <td>487804</td>\n",
              "      <td>130666</td>\n",
              "      <td>10349</td>\n",
              "      <td>878949</td>\n",
              "      <td>Nigeria</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>0</td>\n",
              "      <td>290333</td>\n",
              "      <td>183998</td>\n",
              "      <td>16892</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6765</td>\n",
              "      <td>696</td>\n",
              "      <td>1110</td>\n",
              "      <td>1410285</td>\n",
              "      <td>...</td>\n",
              "      <td>4675846</td>\n",
              "      <td>3755613</td>\n",
              "      <td>418132</td>\n",
              "      <td>399234</td>\n",
              "      <td>4287787</td>\n",
              "      <td>3260821</td>\n",
              "      <td>1160598</td>\n",
              "      <td>31988</td>\n",
              "      <td>1425274</td>\n",
              "      <td>Tanzania</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2153961</td>\n",
              "      <td>407257</td>\n",
              "      <td>7731</td>\n",
              "      <td>0</td>\n",
              "      <td>16155</td>\n",
              "      <td>...</td>\n",
              "      <td>7100010</td>\n",
              "      <td>1569480</td>\n",
              "      <td>65075</td>\n",
              "      <td>920736</td>\n",
              "      <td>557223</td>\n",
              "      <td>557207</td>\n",
              "      <td>131276</td>\n",
              "      <td>7296</td>\n",
              "      <td>182200</td>\n",
              "      <td>Uganda</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1338583</td>\n",
              "      <td>540167</td>\n",
              "      <td>281639</td>\n",
              "      <td>85145</td>\n",
              "      <td>15316485</td>\n",
              "      <td>7554171</td>\n",
              "      <td>918639</td>\n",
              "      <td>502608</td>\n",
              "      <td>16166156</td>\n",
              "      <td>...</td>\n",
              "      <td>37758802</td>\n",
              "      <td>21408476</td>\n",
              "      <td>6163738</td>\n",
              "      <td>4716669</td>\n",
              "      <td>13150256</td>\n",
              "      <td>17138615</td>\n",
              "      <td>7685768</td>\n",
              "      <td>455014</td>\n",
              "      <td>35469650</td>\n",
              "      <td>Other</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7 rows × 31 columns</p>\n",
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    {
      "cell_type": "code",
      "source": [
        "results_path = os.path.join(fp, 'Outputs/all_agglomerations_unelectrified_population.csv')\n",
        "\n",
        "if os.path.exists(results_path):\n",
        "    results = pd.read_csv(results_path)\n",
        "\n",
        "# Define population size categories\n",
        "population_bins = [20000, 50000, 100000, 250000, 500000, 1000000, 5000000, float('inf')]\n",
        "population_labels = [\n",
        "    '20,000-50,000\\ninhabitants',\n",
        "    '50,000-100,000\\ninhabitants',\n",
        "    '100,000-250,000\\ninhabitants',\n",
        "    '250,000-500,000\\ninhabitants',\n",
        "    '500,000-1 million\\ninhabitants',\n",
        "    '1-5 million\\ninhabitants',\n",
        "    '5 million +\\ninhabitants'\n",
        "]\n",
        "\n",
        "# Create a new column for population size categories\n",
        "results['Population_Category'] = pd.cut(results['Pop2015'], bins=population_bins, labels=population_labels)\n",
        "\n",
        "# Remove rows with NaN values in 'Population_Category'\n",
        "results = results.dropna(subset=['Population_Category'])\n",
        "\n",
        "# Define the radii in kilometers\n",
        "radii_km = [5, 10, 20, 30, 50]\n",
        "radius_labels = [f'{radius} km' for radius in radii_km]\n",
        "\n",
        "# Filter results for the specified radii\n",
        "results_filtered = results[results['Radius_Kilometers'].isin(radii_km)]\n",
        "\n",
        "# Define custom colors\n",
        "colors = {\n",
        "    5: '#002626',\n",
        "    10: '#229665',\n",
        "    20: '#F49D37',\n",
        "    30: '#931621',\n",
        "    50: '#0CA2D4'\n",
        "}\n",
        "\n",
        "# Initialize the plot\n",
        "plt.figure(figsize=(16, 9))\n",
        "\n",
        "# Plot the box plot for each radius, grouped by population category\n",
        "sns.boxplot(\n",
        "    x='Population_Category',\n",
        "    y='Electrification_Rate',\n",
        "    hue='Radius_Kilometers',\n",
        "    data=results_filtered,\n",
        "    whis=0,  # Remove whiskers\n",
        "    fliersize=0,\n",
        "    palette=colors,\n",
        "    width=0.6\n",
        ")\n",
        "\n",
        "# Customize plot\n",
        "plt.xlabel('Urban population hierarchy', fontsize=14, fontname='sans-serif', labelpad=20)\n",
        "plt.ylabel('Electrification rate', fontsize=14, fontname='sans-serif')\n",
        "plt.ylim(0, 1)\n",
        "plt.grid(True, which='both', linestyle='--', linewidth=0.5)\n",
        "\n",
        "# Customize tick labels\n",
        "plt.xticks(fontsize=12, fontname='sans-serif', rotation=0)\n",
        "plt.yticks(fontsize=12, fontname='sans-serif')\n",
        "ax = plt.gca()\n",
        "\n",
        "# Format y-axis labels as percentages\n",
        "ax.set_yticklabels([f'{int(x*100)}%' for x in ax.get_yticks()])\n",
        "\n",
        "# Move the legend to the top right of the plot area\n",
        "legend = ax.legend(title='Radius (km)', fontsize=12, title_fontsize=12, loc='upper left', bbox_to_anchor=(1, 1), frameon=False)\n",
        "\n",
        "# Save the plot\n",
        "path_save = fp + 'Graphics/elec_along_urban_hierarchy.png'\n",
        "plt.savefig(path_save, dpi=500, bbox_inches='tight')\n",
        "\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 930
        },
        "id": "Bmt7zgFaQgHx",
        "outputId": "d693d35f-7865-4c9c-d2f3-6b35df857742"
      },
      "id": "Bmt7zgFaQgHx",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-16-04c9ae01eb62>:67: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
            "  ax.set_yticklabels([f'{int(x*100)}%' for x in ax.get_yticks()])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x900 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4MnkYTpuOhwU",
      "metadata": {
        "id": "4MnkYTpuOhwU"
      },
      "source": [
        "##Distribution of the unelectrified in terms of physical distance"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def calculate_figure(dataframe, calculation_type, urca_categories, country='all'):\n",
        "    # Ensure urca_categories is a list\n",
        "    if not isinstance(urca_categories, list):\n",
        "        urca_categories = [urca_categories]\n",
        "\n",
        "    # Filter data for specific country or all countries\n",
        "    if country != 'all':\n",
        "        df_filtered = dataframe[dataframe['Country'] == country]\n",
        "    else:\n",
        "        df_filtered = dataframe\n",
        "\n",
        "    # Sum data if 'all' countries are selected\n",
        "    if country == 'all':\n",
        "        df_filtered = df_filtered.sum(numeric_only=True).to_frame().transpose()\n",
        "\n",
        "    # Calculate figures based on calculation type\n",
        "    if calculation_type == 'urbanization_rate':\n",
        "        total_population = df_filtered[[f'{i}_Pop' for i in urca_categories]].sum().sum()\n",
        "        urban_population = df_filtered[[f'{i}_Urban_Pop' for i in urca_categories]].sum().sum()\n",
        "        result = (urban_population / total_population) * 100\n",
        "        metric_name = 'urbanization rate'\n",
        "    elif calculation_type == 'electrification_rate':\n",
        "        total_population = df_filtered[[f'{i}_Pop' for i in urca_categories]].sum().sum()\n",
        "        electrified_population = df_filtered[[f'{i}_Elec_Pop' for i in urca_categories]].sum().sum()\n",
        "        result = (electrified_population / total_population) * 100\n",
        "        metric_name = 'electrification rate'\n",
        "    elif calculation_type == 'total_population':\n",
        "        result = df_filtered[[f'{i}_Pop' for i in urca_categories]].sum().sum()\n",
        "        metric_name = 'total population'\n",
        "    elif calculation_type == 'unelectrified_population':\n",
        "        result = df_filtered[[f'{i}_Unelec_Pop' for i in urca_categories]].sum().sum()\n",
        "        metric_name = 'unelectrified population'\n",
        "    elif calculation_type == 'electrified_population':\n",
        "        result = df_filtered[[f'{i}_Elec_Pop' for i in urca_categories]].sum().sum()\n",
        "        metric_name = 'electrified population'\n",
        "    elif calculation_type == 'unelectrified_population_percent':\n",
        "        total_unelectrified_population = df_filtered[[f'{i}_Unelec_Pop' for i in range(1, 31)]].sum().sum()\n",
        "        unelectrified_population = df_filtered[[f'{i}_Unelec_Pop' for i in urca_categories]].sum().sum()\n",
        "        result = (unelectrified_population / total_unelectrified_population) * 100\n",
        "        metric_name = 'unelectrified population percent'\n",
        "    elif calculation_type == 'cumulative_unelectrified_percent':\n",
        "        cumulative_data = np.zeros(30)\n",
        "        for y in range(30):\n",
        "            cumulative_data[y] = df_filtered[f'{y + 1}_Unelec_Pop'].sum()\n",
        "        cumulative_data = np.cumsum(cumulative_data) / np.sum(cumulative_data)\n",
        "        result = cumulative_data\n",
        "        metric_name = 'cumulative unelectrified percent'\n",
        "    else:\n",
        "        raise ValueError(\"Invalid calculation type. Please use 'urbanization_rate', 'electrification_rate', 'total_population', 'unelectrified_population', 'electrified_population', 'unelectrified_population_percent', or 'cumulative_unelectrified_percent'.\")\n",
        "\n",
        "    # Create and print the result statement\n",
        "    if country == 'all':\n",
        "        country_statement = 'all countries'\n",
        "    else:\n",
        "        country_statement = country\n",
        "\n",
        "    if len(urca_categories) == 1:\n",
        "        urca_statement = f'URCA {urca_categories[0]}'\n",
        "    else:\n",
        "        urca_statement = f'URCA {urca_categories[0]}-{urca_categories[-1]}'\n",
        "\n",
        "    if calculation_type != 'cumulative_unelectrified_percent':\n",
        "        if 'rate' in calculation_type or 'percent' in calculation_type:\n",
        "            print(f\"The {metric_name} for {country_statement} in {urca_statement} is {result:.2f}%.\")\n",
        "        else:\n",
        "            print(f\"The {metric_name} for {country_statement} in {urca_statement} is {result:.2f}.\")\n",
        "    else:\n",
        "        return result"
      ],
      "metadata": {
        "id": "_GC__4pYVhbb"
      },
      "id": "_GC__4pYVhbb",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def calculate_statistics(dists_path, country):\n",
        "    # Load df_distances from its file\n",
        "    df_distances = pd.read_csv(os.path.join(dists_path, f'{country}_Dists.csv'))\n",
        "\n",
        "    # Drop the 'Country' column if it exists\n",
        "    if 'Country' in df_distances.columns:\n",
        "        df_distances = df_distances.drop(columns='Country')\n",
        "\n",
        "    # Initialize a list to hold the statistics for each category\n",
        "    stats_list = []\n",
        "\n",
        "    # Loop through each category in df_distances\n",
        "    for category in df_distances.columns:\n",
        "        # Extract the data for the category\n",
        "        cat_distances = df_distances[category].dropna()\n",
        "\n",
        "        if len(cat_distances) > 0:\n",
        "            # Calculate basic statistics\n",
        "            whislo = cat_distances.min()\n",
        "            q1 = cat_distances.quantile(0.25)\n",
        "            med = cat_distances.median()\n",
        "            q3 = cat_distances.quantile(0.75)\n",
        "            whishi = cat_distances.max()\n",
        "            mean = cat_distances.mean()\n",
        "            ci95_hi = mean + 1.96 * (cat_distances.std() / np.sqrt(len(cat_distances)))\n",
        "            ci95_lo = mean - 1.96 * (cat_distances.std() / np.sqrt(len(cat_distances)))\n",
        "\n",
        "            # Store the results in a dictionary\n",
        "            stats = {\n",
        "                'label': category,\n",
        "                'whislo': whislo,\n",
        "                'q1': q1,\n",
        "                'med': med,\n",
        "                'q3': q3,\n",
        "                'whishi': whishi,\n",
        "                'mean': mean,\n",
        "                'ci95_hi': ci95_hi,\n",
        "                'ci95_lo': ci95_lo,\n",
        "                'fliers': []  # Optional: add outliers if you want to handle them\n",
        "            }\n",
        "        else:\n",
        "            # Insert an n/a value for the missing data\n",
        "            stats = {\n",
        "                'label': category,\n",
        "                'whislo': np.nan,\n",
        "                'q1': np.nan,\n",
        "                'med': np.nan,\n",
        "                'q3': np.nan,\n",
        "                'whishi': np.nan,\n",
        "                'mean': np.nan,\n",
        "                'ci95_hi': np.nan,\n",
        "                'ci95_lo': np.nan,\n",
        "                'fliers': []  # Optional: add outliers if you want to handle them\n",
        "            }\n",
        "            print(f\"No data for category '{category}', skipping...\")\n",
        "\n",
        "        stats_list.append(stats)\n",
        "\n",
        "    return stats_list"
      ],
      "metadata": {
        "id": "jENHF5XiSgJh"
      },
      "id": "jENHF5XiSgJh",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def calculate_total_statistics(dists_path, countries):\n",
        "    # Initialize an empty DataFrame to hold aggregated distances\n",
        "    aggregated_distances = pd.DataFrame()\n",
        "\n",
        "    # Loop through each country and aggregate the data\n",
        "    for country in countries:\n",
        "        # Load df_distances from its file\n",
        "        df_distances = pd.read_csv(os.path.join(dists_path, f'{country}_Dists.csv'))\n",
        "\n",
        "        # Drop the 'Country' column if it exists\n",
        "        if 'Country' in df_distances.columns:\n",
        "            df_distances = df_distances.drop(columns='Country')\n",
        "\n",
        "        # Append the data to the aggregated DataFrame\n",
        "        aggregated_distances = pd.concat([aggregated_distances, df_distances], axis=0)\n",
        "\n",
        "    # Initialize a list to hold the statistics for each category\n",
        "    stats_list = []\n",
        "\n",
        "    # Loop through each category in aggregated_distances\n",
        "    for category in aggregated_distances.columns:\n",
        "        # Extract the data for the category\n",
        "        cat_distances = aggregated_distances[category].dropna()\n",
        "\n",
        "        if len(cat_distances) > 0:\n",
        "            # Calculate basic statistics\n",
        "            whislo = cat_distances.min()\n",
        "            q1 = cat_distances.quantile(0.25)\n",
        "            med = cat_distances.median()\n",
        "            q3 = cat_distances.quantile(0.75)\n",
        "            whishi = cat_distances.max()\n",
        "            mean = cat_distances.mean()\n",
        "            ci95_hi = mean + 1.96 * (cat_distances.std() / np.sqrt(len(cat_distances)))\n",
        "            ci95_lo = mean - 1.96 * (cat_distances.std() / np.sqrt(len(cat_distances)))\n",
        "\n",
        "            # Store the results in a dictionary\n",
        "            stats = {\n",
        "                'label': category,\n",
        "                'whislo': whislo,\n",
        "                'q1': q1,\n",
        "                'med': med,\n",
        "                'q3': q3,\n",
        "                'whishi': whishi,\n",
        "                'mean': mean,\n",
        "                'ci95_hi': ci95_hi,\n",
        "                'ci95_lo': ci95_lo,\n",
        "                'fliers': []  # Optional: add outliers if you want to handle them\n",
        "            }\n",
        "        else:\n",
        "            # Insert an n/a value for the missing data\n",
        "            stats = {\n",
        "                'label': category,\n",
        "                'whislo': np.nan,\n",
        "                'q1': np.nan,\n",
        "                'med': np.nan,\n",
        "                'q3': np.nan,\n",
        "                'whishi': np.nan,\n",
        "                'mean': np.nan,\n",
        "                'ci95_hi': np.nan,\n",
        "                'ci95_lo': np.nan,\n",
        "                'fliers': []  # Optional: add outliers if you want to handle them\n",
        "            }\n",
        "            print(f\"No data for category '{category}', skipping...\")\n",
        "\n",
        "        stats_list.append(stats)\n",
        "\n",
        "    return stats_list"
      ],
      "metadata": {
        "id": "tyX8vF48eQh8"
      },
      "id": "tyX8vF48eQh8",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def calculate_weighted_average_distance(dists_path, pop_data_df, countries):\n",
        "    # Initialize variables to hold total weighted distance and total unelectrified population\n",
        "    total_weighted_distance = 0\n",
        "    total_unelec_pop = 0\n",
        "\n",
        "    # Loop through each country\n",
        "    for country in countries:\n",
        "        # Load the distance data for the country\n",
        "        df_distances = pd.read_csv(os.path.join(dists_path, f'{country}_Dists.csv'))\n",
        "\n",
        "        # Drop the 'Country' column if it exists\n",
        "        if 'Country' in df_distances.columns:\n",
        "            df_distances = df_distances.drop(columns='Country')\n",
        "\n",
        "        # Calculate the average distance across all URCA categories for this country\n",
        "        mean_distance = df_distances.mean().mean()  # Mean of all columns (URCA categories) for this country\n",
        "\n",
        "        # Get the unelectrified population for the current country\n",
        "        unelec_pop = pop_data_df.loc[pop_data_df['Country'] == country, 'Unelec_Pop'].values[0]\n",
        "        print(f\"{country}: Mean Distance = {mean_distance}, Unelectrified Population = {unelec_pop}\")\n",
        "\n",
        "        # Calculate the weighted distance for this country\n",
        "        weighted_distance = mean_distance * unelec_pop\n",
        "\n",
        "        # Update the total weighted distance and total unelectrified population\n",
        "        total_weighted_distance += weighted_distance\n",
        "        total_unelec_pop += unelec_pop\n",
        "\n",
        "    # Calculate the final weighted average distance across all countries\n",
        "    final_weighted_avg_distance = total_weighted_distance / total_unelec_pop if total_unelec_pop > 0 else np.nan\n",
        "\n",
        "    return final_weighted_avg_distance"
      ],
      "metadata": {
        "id": "weM2Gp2ZmUiv"
      },
      "id": "weM2Gp2ZmUiv",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the output DataFrame\n",
        "dists_path = os.path.join(path, 'Dists')\n",
        "output_path = os.path.join(dists_path, 'dist_output_df.csv')\n",
        "input_path = os.path.join(path, 'Outputs/elec_unelec_output.csv')\n",
        "dist_output_df = pd.read_csv(output_path)\n",
        "pop_data_df = pd.read_csv(input_path)"
      ],
      "metadata": {
        "id": "rXVrghNeGApz"
      },
      "id": "rXVrghNeGApz",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate cumulative electrification rates\n",
        "cumulative_data = calculate_figure(df, 'cumulative_unelectrified_percent', list(range(1, 31)))\n",
        "\n",
        "# Get statistics across all countries\n",
        "plot_data_combined = calculate_total_statistics(dists_path, countries)"
      ],
      "metadata": {
        "id": "Ovj_N2wtYACl"
      },
      "id": "Ovj_N2wtYACl",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the weighted average distance across all countries\n",
        "final_weighted_avg_distance = calculate_weighted_average_distance(dists_path, pop_data_df, countries)\n",
        "\n",
        "print(\"Final Weighted Average Distance:\", final_weighted_avg_distance)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "giyZMKB_oC-m",
        "outputId": "75619875-bb56-42c3-eccd-ea8dec3abab8"
      },
      "id": "giyZMKB_oC-m",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Angola: Mean Distance = 28.64589722629786, Unelectrified Population = 13059607\n",
            "Benin: Mean Distance = 12.706245564441987, Unelectrified Population = 7058777\n",
            "Botswana: Mean Distance = 16.159775830494894, Unelectrified Population = 1107182\n",
            "Burkina Faso: Mean Distance = 16.16988275919312, Unelectrified Population = 13895750\n",
            "Burundi: Mean Distance = 4.6186059404846, Unelectrified Population = 9205390\n",
            "Cameroon: Mean Distance = 18.08095242780049, Unelectrified Population = 14594589\n",
            "Central African Republic: Mean Distance = 30.952140729055916, Unelectrified Population = 3878460\n",
            "Chad: Mean Distance = 42.52137552050526, Unelectrified Population = 12042997\n",
            "DRC: Mean Distance = 46.07129427111417, Unelectrified Population = 67001991\n",
            "ROC: Mean Distance = 14.724738507002067, Unelectrified Population = 1818723\n",
            "CDI: Mean Distance = 8.142260954787165, Unelectrified Population = 11894538\n",
            "Equatorial Guinea: Mean Distance = 7.499132864527568, Unelectrified Population = 621450\n",
            "Eritrea: Mean Distance = 19.739290560581313, Unelectrified Population = 3921473\n",
            "Swaziland: Mean Distance = 2.36035419853082, Unelectrified Population = 843216\n",
            "Ethiopia: Mean Distance = 25.520139403287747, Unelectrified Population = 77759758\n",
            "Gabon: Mean Distance = 11.881834598981317, Unelectrified Population = 987865\n",
            "Gambia: Mean Distance = 4.06450613849285, Unelectrified Population = 1099579\n",
            "Ghana: Mean Distance = 8.114284693058812, Unelectrified Population = 10458908\n",
            "Guinea: Mean Distance = 18.228877548707917, Unelectrified Population = 9089432\n",
            "Guinea-Bissau: Mean Distance = 8.123148000493334, Unelectrified Population = 1354761\n",
            "Kenya: Mean Distance = 19.22824531258927, Unelectrified Population = 30415515\n",
            "Lesotho: Mean Distance = 7.070396434476949, Unelectrified Population = 1650140\n",
            "Liberia: Mean Distance = 15.363632770892837, Unelectrified Population = 2906667\n",
            "Madagascar: Mean Distance = 24.031691384418487, Unelectrified Population = 21913282\n",
            "Malawi: Mean Distance = 8.1448947418963, Unelectrified Population = 13682378\n",
            "Mali: Mean Distance = 34.0663868094298, Unelectrified Population = 12950253\n",
            "Mauritania: Mean Distance = 24.717841280261343, Unelectrified Population = 2481198\n",
            "Mozambique: Mean Distance = 18.414426506498295, Unelectrified Population = 19329114\n",
            "Namibia: Mean Distance = 23.117315858694287, Unelectrified Population = 1119828\n",
            "Niger: Mean Distance = 31.917378607567045, Unelectrified Population = 16581428\n",
            "Nigeria: Mean Distance = 17.102668622082398, Unelectrified Population = 116509472\n",
            "Rwanda: Mean Distance = 4.5845146703248325, Unelectrified Population = 9226586\n",
            "Senegal: Mean Distance = 20.917439121138617, Unelectrified Population = 8323476\n",
            "Sierra Leone: Mean Distance = 14.494832159101096, Unelectrified Population = 4710588\n",
            "Somalia: Mean Distance = 39.922262364224494, Unelectrified Population = 8227032\n",
            "South Africa: Mean Distance = 12.464354966010802, Unelectrified Population = 8441292\n",
            "South Sudan: Mean Distance = 37.51495956263994, Unelectrified Population = 11580375\n",
            "Sudan: Mean Distance = 28.10611595931914, Unelectrified Population = 28112627\n",
            "Tanzania: Mean Distance = 17.969044031468304, Unelectrified Population = 36809769\n",
            "Togo: Mean Distance = 10.644941394654534, Unelectrified Population = 3886682\n",
            "Uganda: Mean Distance = 12.77325695059626, Unelectrified Population = 30567190\n",
            "Zambia: Mean Distance = 22.39133808157249, Unelectrified Population = 9767715\n",
            "Zimbabwe: Mean Distance = 11.348192749492227, Unelectrified Population = 10050537\n",
            "Final Weighted Average Distance: 22.73833559147147\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define custom y-limits for the aggregated plot\n",
        "y_limits_aggregate = (0, 120)  # Example y-limit, adjust as needed\n",
        "\n",
        "# Plot the aggregated results (SSA)\n",
        "fig, ax = plt.subplots(figsize=(14, 4))\n",
        "ax.text(0.01, 0.98, 'SSA', transform=ax.transAxes, fontsize=14, fontweight='bold', va='top', ha='left', fontname='sans-serif')\n",
        "ax.axhline(y=final_weighted_avg_distance, color='black', linewidth=1)\n",
        "\n",
        "# Set custom y-limits for the aggregated plot\n",
        "ax.set_ylim(y_limits_aggregate)\n",
        "\n",
        "# Remove the x-axis labels and ticks initially\n",
        "ax.set_xticks([])\n",
        "ax.set_xticklabels([])\n",
        "ax.tick_params(axis='x', which='both', bottom=False, top=False)  # Ensure no ticks are shown\n",
        "\n",
        "# Create secondary y-axis for cumulative percentage line\n",
        "secax_combined = ax.twinx()\n",
        "secax_combined.set_ylim(0, 1)\n",
        "\n",
        "# Format the secondary y-axis with percentage labels and match font size to primary y-axis\n",
        "secax_combined.set_yticks([0, 0.2, 0.4, 0.6, 0.8, 1.0])\n",
        "secax_combined.set_yticklabels(['0%', '20%', '40%', '60%', '80%', '100%'], fontsize=14)\n",
        "\n",
        "# Plot the cumulative average area plot first (behind the box plots)\n",
        "secax_combined.fill_between(range(1, 31), cumulative_avg, color='#002626', alpha=0.2, zorder=1)\n",
        "\n",
        "# Plot the combined box-and-whiskers plot on top of the area plot\n",
        "for idx, data in enumerate(plot_data_combined):\n",
        "    if not np.isnan(data['mean']):\n",
        "        ax.bxp([data], positions=[idx + 1], showmeans=True, meanline=True, showcaps=False, showbox=True,\n",
        "               patch_artist=True,\n",
        "               widths=0.5,\n",
        "               boxprops=dict(facecolor='lightblue', edgecolor='black', linewidth=1),\n",
        "               medianprops=dict(color='none'), meanprops=dict(color='black'), whiskerprops=dict(color='none'), capprops=dict(color='none'),\n",
        "               zorder=2)  # Ensure box plots are on top\n",
        "    else:\n",
        "        ax.text(idx + 1, y_limits_aggregate[1] * 0.5, '', ha='center', va='center', color='red', fontsize=10)\n",
        "\n",
        "ax.axvline(x=7.5, color='black', linewidth=.5, zorder=1)\n",
        "ax.axvline(x=14.5, color='black', linewidth=.5, zorder=1)\n",
        "ax.axvline(x=21.5, color='black', linewidth=.5, zorder=1)\n",
        "ax.axvline(x=28.5, color='black', linewidth=.5, zorder=1)\n",
        "\n",
        "# Repress x-axis labels\n",
        "ax.set_xticklabels([])\n",
        "\n",
        "# Add the bottom labels closer to the axis\n",
        "for j, label in enumerate(np.arange(1, 31)):\n",
        "    ax.text(j + 1, 1.02, label, ha='center', va='bottom', rotation=0, transform=ax.get_xaxis_transform())\n",
        "\n",
        "# Enable tick marks on the bottom x-axis\n",
        "ax.tick_params(axis='x', which='major', bottom=False, top=True)\n",
        "\n",
        "# Add label below the bottom x-axis\n",
        "plt.text(15, 1.15, 'URCA category', ha='center', va='center', fontsize=14)\n",
        "\n",
        "# Save the plot\n",
        "path_save = fp + 'Graphics/SSA_dist.png'\n",
        "plt.savefig(path_save, dpi=500, bbox_inches='tight')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "id": "SogwSJvlfKd2",
        "outputId": "0d7cd19f-4e2a-4778-a271-a13b637202a9"
      },
      "id": "SogwSJvlfKd2",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x300 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the specified countries and their custom y-limits\n",
        "\n",
        "y_limits = {\n",
        "    'DRC': (0, 240),\n",
        "    'Ethiopia': (0, 125),\n",
        "    'Tanzania': (0, 80),\n",
        "    'Nigeria': (0, 80),\n",
        "    'Kenya': (0, 100),\n",
        "    'Uganda': (0, 60)\n",
        "}\n",
        "\n",
        "# Create arrays to hold cumulative data\n",
        "cumulative_data = np.zeros((len(country_labels), 30))\n",
        "i = 0\n",
        "\n",
        "for country in country_labels:\n",
        "    # Calculate average weighted distance\n",
        "    country_data = dist_output_df[dist_output_df['Country'] == country]\n",
        "    average_weighted_distance = country_data['Average_Weighted_Distance'].iloc[0]\n",
        "\n",
        "    # Calculate cumulative electrification rates\n",
        "    cumulative_data[i, :] = calculate_figure(df, 'cumulative_unelectrified_percent', list(range(1, 31)), country=country)\n",
        "\n",
        "    # Get statistics using the new function\n",
        "    plot_data_combined = calculate_statistics(dists_path, country)\n",
        "\n",
        "    # Plot combined countries plot (SSA)\n",
        "    fig, ax = plt.subplots(figsize=(14, 4))\n",
        "    ax.text(0.01, 0.98, country, transform=ax.transAxes, fontsize=14, fontweight='bold', va='top', ha='left', fontname='sans-serif')\n",
        "    ax.axhline(y=average_weighted_distance, color='black', linewidth=1)\n",
        "\n",
        "    # Set custom y-limits\n",
        "    if country in y_limits:\n",
        "        ax.set_ylim(y_limits[country])\n",
        "\n",
        "    # Remove the x-axis labels and ticks for all plots except the last\n",
        "    if country != country_labels[-1]:\n",
        "        ax.set_xticks([])\n",
        "        ax.set_xticklabels([])\n",
        "        ax.tick_params(axis='x', which='both', bottom=False, top=False)  # Ensure no ticks are shown\n",
        "\n",
        "    # Create secondary y-axis for cumulative percentage line\n",
        "    secax_combined = ax.twinx()\n",
        "    secax_combined.set_ylim(0, 1)\n",
        "\n",
        "    # Format the secondary y-axis with percentage labels and match font size to primary y-axis\n",
        "    secax_combined.set_yticks([0, 0.2, 0.4, 0.6, 0.8, 1.0])\n",
        "    secax_combined.set_yticklabels(['0%', '20%', '40%', '60%', '80%', '100%'], fontsize=14)\n",
        "\n",
        "    # Plot the cumulative average area plot first (behind the box plots)\n",
        "    secax_combined.fill_between(range(1, 31), cumulative_data[i], color='#002626', alpha=0.2, zorder=1)\n",
        "\n",
        "    # Plot the combined box-and-whiskers plot on top of the area plot\n",
        "    for idx, data in enumerate(plot_data_combined):\n",
        "        if not np.isnan(data['mean']):\n",
        "            ax.bxp([data], positions=[idx + 1], showmeans=True, meanline=True, showcaps=False, showbox=True,\n",
        "                   patch_artist=True,\n",
        "                   widths=0.5,\n",
        "                   boxprops=dict(facecolor='lightblue', edgecolor='black', linewidth=1),\n",
        "                   medianprops=dict(color='none'), meanprops=dict(color='black'), whiskerprops=dict(color='none'), capprops=dict(color='none'),\n",
        "                   zorder=2)  # Ensure box plots are on top\n",
        "        else:\n",
        "            ax.text(idx + 1, y_limits[country][1] * 0.5, '', ha='center', va='center', color='red', fontsize=10)\n",
        "\n",
        "    ax.axvline(x=7.5, color='black', linewidth=.5, zorder=1)\n",
        "    ax.axvline(x=14.5, color='black', linewidth=.5, zorder=1)\n",
        "    ax.axvline(x=21.5, color='black', linewidth=.5, zorder=1)\n",
        "    ax.axvline(x=28.5, color='black', linewidth=.5, zorder=1)\n",
        "\n",
        "    # Repress x-axis labels\n",
        "    ax.set_xticklabels([])\n",
        "\n",
        "    # Apply specific x-axis formatting to the last plot only\n",
        "    if country == country_labels[-1]:\n",
        "\n",
        "        # Add the bottom labels closer to the axis\n",
        "        for j, label in enumerate(x_axis_labels):\n",
        "            ax.text(j + 1, -0.02, label, ha='center', va='top', rotation=90, transform=ax.get_xaxis_transform())\n",
        "\n",
        "        # Add center-justified text below lower axis labels\n",
        "        top_labels = ['Within urban core', '<1 hour to city', '1-2 hours to city', '2-3 hours to city']\n",
        "        top_positions = [4, 11, 18, 25]\n",
        "        for j, label in enumerate(top_labels):\n",
        "            plt.text(top_positions[j], -0.68, label, ha='center', va='center', fontsize=14, transform=ax.get_xaxis_transform())\n",
        "\n",
        "        # Enable tick marks on the bottom x-axis\n",
        "        ax.tick_params(axis='x', which='major', bottom=True, top = False)\n",
        "\n",
        "        # Add label below the bottom x-axis\n",
        "        plt.text(15, -0.8, 'Distance from city of reference based on population', ha='center', va='center', fontsize=14)\n",
        "\n",
        "    # Save the plot\n",
        "    path_save = os.path.join(fp, f'Graphics/{country}_dist.png')\n",
        "    plt.savefig(path_save, dpi=500, bbox_inches='tight')\n",
        "\n",
        "    plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "3xjh9AkqS0-G",
        "outputId": "160cf945-211e-4749-e991-2a1b1502f142"
      },
      "id": "3xjh9AkqS0-G",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x300 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No data for category '1', skipping...\n",
            "No data for category '2', skipping...\n",
            "No data for category '3', skipping...\n",
            "No data for category '8', skipping...\n",
            "No data for category '10', skipping...\n",
            "No data for category '15', skipping...\n",
            "No data for category '22', skipping...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x300 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No data for category '1', skipping...\n",
            "No data for category '2', skipping...\n",
            "No data for category '9', skipping...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x300 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No data for category '1', skipping...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x300 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No data for category '1', skipping...\n",
            "No data for category '2', skipping...\n",
            "No data for category '3', skipping...\n",
            "No data for category '4', skipping...\n",
            "No data for category '8', skipping...\n",
            "No data for category '10', skipping...\n",
            "No data for category '15', skipping...\n",
            "No data for category '17', skipping...\n",
            "No data for category '22', skipping...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x300 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No data for category '1', skipping...\n",
            "No data for category '2', skipping...\n",
            "No data for category '3', skipping...\n",
            "No data for category '4', skipping...\n",
            "No data for category '8', skipping...\n",
            "No data for category '15', skipping...\n",
            "No data for category '22', skipping...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x300 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4s9AIalaP477",
      "metadata": {
        "id": "4s9AIalaP477"
      },
      "source": [
        "## Unelectrified population by URCA category, country-level\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "DkdPcwXMQngt",
      "metadata": {
        "id": "DkdPcwXMQngt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "adf73c58-3d8d-4056-b2be-e5183da2479a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-118-24736020abdd>:29: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
            "  graph_6_b[z, y] = df[df['Country'] == countries[z]][f'{y+1}_Rural_Unelec_Pop']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DRC\n",
            "Ethiopia\n",
            "Tanzania\n",
            "Nigeria\n",
            "Kenya\n",
            "Uganda\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "IndexError",
          "evalue": "list index out of range",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-118-24736020abdd>\u001b[0m in \u001b[0;36m<cell line: 16>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     51\u001b[0m         \u001b[0mnum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcountry_codes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcountry_labels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 53\u001b[0;31m         \u001b[0mscatter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgraph_6_a\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgraph_6_b\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m20000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcountry_labels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.7\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     55\u001b[0m     \u001b[0;31m# Move x-axis labels to the top\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mIndexError\u001b[0m: list index out of range"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x800 with 1 Axes>"
            ],
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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Define the file paths and load the CSV files\n",
        "df_path = os.path.join(path, 'Outputs/rural_urban_elec_unelec_URCA_output.csv')\n",
        "df_path1 = os.path.join(path, 'Outputs/elec_unelec_output.csv')\n",
        "\n",
        "if os.path.exists(df_path):\n",
        "    df = pd.read_csv(df_path)\n",
        "else:\n",
        "    print(f\"File not found: {df_path}\")\n",
        "\n",
        "if os.path.exists(df_path1):\n",
        "    df1 = pd.read_csv(df_path1)\n",
        "else:\n",
        "    print(f\"File not found: {df_path1}\")\n",
        "\n",
        "# Check if the dataframes were loaded correctly\n",
        "if 'df' in locals() and 'df1' in locals():\n",
        "    # Initialize arrays\n",
        "    graph_6_a = np.zeros((len(countries), 30))  # rows = # of countries, columns = 30 URCA categories\n",
        "    graph_6_b = np.zeros((len(countries), 30))  # rows = # of countries, columns = 30 URCA categories\n",
        "    graph_6_c = np.zeros((len(countries), 1))   # number of countries, column = 1\n",
        "\n",
        "    # Specify array with total unelectrified population\n",
        "    graph_6_c = df1['Unelec_Pop'].values\n",
        "\n",
        "    # Specify array with absolute unelectrified population by URCA category\n",
        "    for z in range(len(countries)):\n",
        "        for y in range(30):\n",
        "            # Total unelectrified\n",
        "            graph_6_b[z, y] = df[df['Country'] == countries[z]][f'{y+1}_Rural_Unelec_Pop']\n",
        "\n",
        "    # Specify array with unelectrified population by URCA category as % of total unelectrified\n",
        "    for z in range(len(countries)):\n",
        "        for y in range(30):\n",
        "            # Unelectrified per URCA category as % of total unelectrified\n",
        "            graph_6_a[z, y] = graph_6_b[z, y] / graph_6_c[z]\n",
        "\n",
        "    fig, ax = plt.subplots(figsize=(15, 8))\n",
        "\n",
        "    # Add vertical lines demarcating 'tipping points'\n",
        "    plt.axvline(7.5, 500000, 0, color='#9C9990', zorder=0)\n",
        "    plt.axvline(14.5, 500000, 0, color='#9C9990', zorder=0)\n",
        "    plt.axvline(21.5, 500000, 0, color='#9C9990', zorder=0)\n",
        "    plt.axvline(28.5, 500000, 0, color='#9C9990', zorder=0)\n",
        "\n",
        "    labels = np.arange(1, 31)\n",
        "    # Plot all countries in gray\n",
        "    for l in range(len(countries)):\n",
        "        plt.scatter(labels, graph_6_a[l, :], s=graph_6_b[l, :] / 20000, color='#D8D4D5')\n",
        "\n",
        "    for p in range(len(country_codes)):\n",
        "        num = country_codes[p]\n",
        "        print(country_labels[p])\n",
        "        scatter = plt.scatter(labels, graph_6_a[num, :], s=graph_6_b[num, :] / 20000, label=country_labels[p], color=col[p], alpha=0.7)\n",
        "\n",
        "    # Move x-axis labels to the top\n",
        "    ax.xaxis.set_label_position('top')\n",
        "    ax.xaxis.tick_top()\n",
        "\n",
        "    plt.xticks(labels)\n",
        "    plt.xlim(0.5, 30.5)\n",
        "    plt.ylim(0, 0.7)\n",
        "    plt.ylabel('Percent of total unelectrified', fontsize=14)\n",
        "    vals = ax.get_yticks()\n",
        "    ax.set_yticklabels(['{:,.0%}'.format(x) for x in vals])\n",
        "    plt.xlabel('URCA category', fontsize=14)\n",
        "\n",
        "    # Add secondary x-axis for custom labels\n",
        "    ax2 = ax.secondary_xaxis('bottom')\n",
        "    ax2.set_xticks(labels)\n",
        "    ax2.set_xticklabels(x_axis_labels, rotation=90, fontsize=14)\n",
        "\n",
        "    # Add center-justified text below lower axis labels\n",
        "    plt.text(4, -.25, 'Within urban core', ha='center', va='center', fontsize=14)\n",
        "    plt.text(11, -.25, '<1 hour to city', ha='center', va='center', fontsize=14)\n",
        "    plt.text(18, -.25, '1-2 hours from city', ha='center', va='center', fontsize=14)\n",
        "    plt.text(25, -.25, '2-3 hours from city', ha='center', va='center', fontsize=14)\n",
        "\n",
        "    # Add label below the bottom x-axis\n",
        "    plt.text(15, -.3, 'Distance from city of reference based on population', ha='center', va='center', fontsize=14)\n",
        "\n",
        "    # Create random scatterplot representing maximum range\n",
        "    scatter = ax.scatter(labels, np.full((1, 30), -5), s=graph_6_b[30, :] / 20000)\n",
        "\n",
        "    kw = dict(prop='sizes', num=6, color='#D8D4D5', fmt=\"{x:,.0f}\", func=lambda s: s * 20000 / 1000000)\n",
        "\n",
        "    lgd2 = ax.legend(*scatter.legend_elements(**kw), bbox_to_anchor=(1, .55), frameon=False, labelspacing=1.75, title='Unelectrified population\\n(millions)')\n",
        "\n",
        "    # Center align the legend title\n",
        "    lgd2.get_title().set_ha('center')\n",
        "\n",
        "    path_save = (fp + 'Graphics/' + 'Unelectrified_Scatterplot.png')\n",
        "    plt.savefig(path_save, bbox_inches='tight', dpi=300)\n",
        "else:\n",
        "    print(\"DataFrames not defined. Check file paths and loading process.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "znKJrYB-buBU",
      "metadata": {
        "id": "znKJrYB-buBU"
      },
      "source": [
        "## Unelectrified population physical distance from city centers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "zPCFG_GSItig",
      "metadata": {
        "id": "zPCFG_GSItig",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fa6d18e4-f1ca-4837-c0c5-3312403ed2ce"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing Angola\n",
            "Processing Benin\n",
            "Processing Botswana\n",
            "Processing Burkina Faso\n",
            "Processing Burundi\n",
            "Processing Cameroon\n",
            "Processing Central African Republic\n",
            "Processing Chad\n",
            "Processing DRC\n",
            "Processing ROC\n",
            "Processing CDI\n",
            "Processing Equatorial Guinea\n",
            "Processing Eritrea\n",
            "Processing Swaziland\n",
            "Processing Ethiopia\n",
            "Processing Gabon\n",
            "Processing Gambia\n",
            "Processing Ghana\n",
            "Processing Guinea\n",
            "Processing Guinea-Bissau\n",
            "Processing Kenya\n",
            "Processing Lesotho\n",
            "Processing Liberia\n",
            "Processing Madagascar\n",
            "Processing Malawi\n",
            "Processing Mali\n",
            "Processing Mauritania\n",
            "Processing Mozambique\n",
            "Processing Namibia\n",
            "Processing Niger\n",
            "Processing Nigeria\n",
            "Processing Rwanda\n",
            "Processing Senegal\n",
            "Processing Sierra Leone\n",
            "Processing Somalia\n",
            "Processing South Africa\n",
            "Processing South Sudan\n",
            "Processing Sudan\n",
            "Processing Tanzania\n",
            "Processing Togo\n",
            "Processing Uganda\n",
            "Processing Zambia\n",
            "Processing Zimbabwe\n",
            "Combined statistics saved to /content/drive/My Drive/Rurban/Dists/combined_stats.csv\n",
            "  Country URCA Category               Mean             Median  \\\n",
            "0  Angola             4 1.1652472443079385                1.0   \n",
            "1  Angola             5 11.122462002504413                1.0   \n",
            "2  Angola             6 28.320947706711017  23.51579823506593   \n",
            "3  Angola             7  41.49019367537985 31.642570182385636   \n",
            "4  Angola             8  4.813337397387217 3.6055512754639896   \n",
            "\n",
            "             Std Dev  Min                25%                50%  \\\n",
            "0 0.3415283814279714  1.0                1.0                1.0   \n",
            "1  17.63852207890152  1.0                1.0                1.0   \n",
            "2 25.233250902471468  1.0                1.0  23.51579823506593   \n",
            "3  37.64391617924626  1.0 1.4142135623730951 31.642570182385636   \n",
            "4 3.7278394903076806  1.0                2.0 3.6055512754639896   \n",
            "\n",
            "                 75%               Max  Count  \n",
            "0 1.2071067811865475  2.23606797749979     15  \n",
            "1   2.23606797749979 47.16990566028301     57  \n",
            "2  54.33447065394114 75.47184905645284     98  \n",
            "3  72.12661004597425  149.093930124603    108  \n",
            "4                7.0  19.4164878389476   2829  \n"
          ]
        }
      ],
      "source": [
        "# Initialize an empty list to hold the processed data\n",
        "stats_list = []\n",
        "\n",
        "# Process each country's data\n",
        "for cntry in countries:\n",
        "    print(f'Processing {cntry}')\n",
        "    dist_file_path = os.path.join(path, f'Dists/{cntry}_Dists.csv')\n",
        "\n",
        "    if os.path.exists(dist_file_path):\n",
        "        # Read the distance data for the current country\n",
        "        dist_df = pd.read_csv(dist_file_path)\n",
        "\n",
        "        # Ensure URCA categories are treated as strings for consistency\n",
        "        dist_df.columns = dist_df.columns.astype(str)\n",
        "\n",
        "        # Extract statistical values for each URCA category\n",
        "        for urca_category in dist_df.columns:\n",
        "            urca_data = dist_df[urca_category].dropna()\n",
        "            if not urca_data.empty:\n",
        "                stats = {\n",
        "                    'Country': cntry,\n",
        "                    'URCA Category': urca_category,\n",
        "                    'Mean': urca_data.mean(),\n",
        "                    'Median': urca_data.median(),\n",
        "                    'Std Dev': urca_data.std(),\n",
        "                    'Min': urca_data.min(),\n",
        "                    '25%': urca_data.quantile(0.25),\n",
        "                    '50%': urca_data.quantile(0.5),\n",
        "                    '75%': urca_data.quantile(0.75),\n",
        "                    'Max': urca_data.max(),\n",
        "                    'Count': urca_data.count()  # Add count of data points\n",
        "                }\n",
        "                stats_list.append(stats)\n",
        "    else:\n",
        "        print(f'File for {cntry} not found.')\n",
        "\n",
        "# Combine all the statistics into a single DataFrame\n",
        "if stats_list:\n",
        "    stats_df = pd.DataFrame(stats_list)\n",
        "    # Save the combined statistics to a CSV file\n",
        "    output_path = os.path.join(path, 'Dists/combined_stats.csv')\n",
        "    stats_df.to_csv(output_path, index=False)\n",
        "    print(f'Combined statistics saved to {output_path}')\n",
        "    print(stats_df.head())\n",
        "else:\n",
        "    print('No valid data found for any country.')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Read the combined statistics\n",
        "combined_stats_path = os.path.join(path, 'Dists/combined_stats.csv')\n",
        "summary_stats = pd.read_csv(combined_stats_path)"
      ],
      "metadata": {
        "id": "LsTOQpJRND0p"
      },
      "id": "LsTOQpJRND0p",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Prepare data for plotting\n",
        "plot_data = []\n",
        "categories = summary_stats['URCA Category'].unique()\n",
        "\n",
        "for idx, category in enumerate(categories):\n",
        "    cat_data = summary_stats[summary_stats['URCA Category'] == category]\n",
        "    if not cat_data.empty:\n",
        "        box_data = {\n",
        "            'label': category,\n",
        "            'whislo': cat_data['Min'].values[0],\n",
        "            'q1': cat_data['25%'].values[0],\n",
        "            'med': cat_data['50%'].values[0],\n",
        "            'q3': cat_data['75%'].values[0],\n",
        "            'whishi': cat_data['Max'].values[0],\n",
        "            'mean': cat_data['Mean'].values[0],\n",
        "            'ci95_hi': cat_data['Mean'].values[0] + 1.96 * (cat_data['Std Dev'].values[0] / cat_data['Count'].values[0]**0.5),\n",
        "            'ci95_lo': cat_data['Mean'].values[0] - 1.96 * (cat_data['Std Dev'].values[0] / cat_data['Count'].values[0]**0.5),\n",
        "            'fliers': []\n",
        "        }\n",
        "        plot_data.append(box_data)\n",
        "\n",
        "# Sort the plot data based on URCA categories to ensure correct order\n",
        "plot_data_sorted = sorted(plot_data, key=lambda x: int(x['label']))"
      ],
      "metadata": {
        "id": "GPZtJLkBUatg"
      },
      "id": "GPZtJLkBUatg",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "categories = [str(i) for i in range(1, 31)]  # URCA categories 1 to 30\n",
        "\n",
        "# List to store melted data for each country\n",
        "melted_data_list = []\n",
        "\n",
        "# Process each country's data and store the melted data\n",
        "for cntry in country_labels:\n",
        "    print(f'Processing {cntry}')\n",
        "    dist_file_path = os.path.join(path, f'Dists/{cntry}_Dists.csv')\n",
        "\n",
        "    if os.path.exists(dist_file_path):\n",
        "        # Read the distance data for the current country\n",
        "        dist_df = pd.read_csv(dist_file_path)\n",
        "\n",
        "        # Ensure URCA categories are treated as strings for consistency\n",
        "        dist_df.columns = dist_df.columns.astype(str)\n",
        "\n",
        "        # Melt the DataFrame to have URCA categories in one column and distances in another\n",
        "        dist_melted = dist_df.melt(var_name='URCA Category', value_name='Distance (km)')\n",
        "\n",
        "        # Add a column for the country\n",
        "        dist_melted['Country'] = cntry\n",
        "\n",
        "        # Drop rows with NaN values\n",
        "        dist_melted = dist_melted.dropna(subset=['Distance (km)'])\n",
        "\n",
        "        # Append the melted data to the list\n",
        "        melted_data_list.append(dist_melted)\n",
        "    else:\n",
        "        print(f'File for {cntry} not found.')\n",
        "\n",
        "# Combine all melted data into a single DataFrame for the combined plot\n",
        "combined_data = pd.concat(melted_data_list, ignore_index=True)"
      ],
      "metadata": {
        "id": "xwtBMrDMuTeI"
      },
      "id": "xwtBMrDMuTeI",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the file path and load the CSV file\n",
        "df_path = os.path.join(path, 'Outputs/rural_urban_elec_unelec_URCA_output.csv')\n",
        "\n",
        "if os.path.exists(df_path):\n",
        "    df = pd.read_csv(df_path)\n",
        "\n",
        "# Custom user-input array for y-limits\n",
        "custom_y_limits = [125, 225, 125, 125, 125, 125, 125]  # Replace with your desired y-limits for each plot\n",
        "\n",
        "# Create arrays to hold cumulative data\n",
        "cumulative_elec = np.zeros(30)  # For total electrified population\n",
        "cumulative_unelec = np.zeros(30)  # For total unelectrified population\n",
        "electrification_rate = np.zeros(30)  # For electrification rate\n",
        "\n",
        "# Loop through each category (1-30) and sum the electrified and unelectrified populations\n",
        "for y in range(30):\n",
        "    # Sum the electrified population for the current category across all countries\n",
        "    electrified_pop = df[f'{y+1}_Elec_Pop'].sum()\n",
        "\n",
        "    # Sum the unelectrified population for the current category across all countries\n",
        "    unelectrified_pop = df[f'{y+1}_Unelec_Pop'].sum()\n",
        "\n",
        "    # Store the sums in the cumulative arrays\n",
        "    cumulative_elec[y] = electrified_pop\n",
        "    cumulative_unelec[y] = unelectrified_pop\n",
        "\n",
        "    # Calculate the electrification rate\n",
        "    total_pop = electrified_pop + unelectrified_pop\n",
        "    if total_pop > 0:\n",
        "        electrification_rate[y] = electrified_pop / total_pop\n",
        "    else:\n",
        "        electrification_rate[y] = 0\n",
        "\n",
        "# Calculate cumulative percentage of unelectrified population correctly\n",
        "graph_4_a = np.zeros((len(country_labels), 30))  # rows = # of countries, column = 30 URCA categories\n",
        "graph_4_b = np.zeros((len(country_labels), 30))  # rows = # of countries, column = 30 URCA categories\n",
        "total_unelec = np.zeros(len(country_labels))\n",
        "\n",
        "# Extract the data series needed (rural population, urban population)\n",
        "for z in range(len(country_labels)):\n",
        "    for c in range(30):\n",
        "        sum1 = df[df['Country'] == country_labels[z]][f'{c+1}_Urban_Unelec_Pop'].values[0]\n",
        "        sum2 = df[df['Country'] == country_labels[z]][f'{c+1}_Rural_Unelec_Pop'].values[0]\n",
        "\n",
        "        total_unelec[z] += sum1 + sum2\n",
        "\n",
        "        # Create array with unelectrified population by URCA category\n",
        "        graph_4_a[z, c] = sum1 + sum2\n",
        "\n",
        "    graph_4_a[z, :] = graph_4_a[z, :] / total_unelec[z]\n",
        "    graph_4_b[z, 0] = graph_4_a[z, 0]\n",
        "\n",
        "    for d in range(1, 30):\n",
        "        graph_4_b[z, d] = graph_4_a[z, 0:d+1].sum()\n",
        "\n",
        "    graph_4_b[z, 29] = 1\n",
        "\n",
        "cumulative_percent = np.mean(graph_4_b, axis=0)\n",
        "\n",
        "# Define figure and axes\n",
        "fig, axes = plt.subplots(len(country_labels) + 1, 1, figsize=(14, 15), gridspec_kw={'height_ratios': [1] * (len(country_labels) + 1)})\n",
        "\n",
        "# Combined countries plot (SSA)\n",
        "axes[0].text(0.01, 0.98, 'SSA', transform=axes[0].transAxes, fontsize=14, fontweight='bold', va='top', ha='left', fontname='sans-serif')\n",
        "axes[0].set_ylim(0, custom_y_limits[0])\n",
        "axes[0].axhline(y=10, color='black', linestyle='-', linewidth=1)  # Add horizontal line at 10 km\n",
        "axes[0].text(1.8, 18, '10 km', fontsize=10, ha='right', va='center', color='black')  # Label the 10 km line\n",
        "\n",
        "# Create secondary y-axis for cumulative percentage line\n",
        "secax_combined = axes[0].twinx()\n",
        "secax_combined.fill_between(range(1, 31), cumulative_percent, color='gray', alpha=0.2, zorder=-1)\n",
        "secax_combined.set_ylim(0, 1)\n",
        "secax_combined.set_yticks([0, 0.25, 0.5, 0.75, 1])\n",
        "secax_combined.set_yticklabels(['0%', '25%', '50%', '75%', '100%'], fontname='sans-serif', fontsize=12)\n",
        "\n",
        "# Plot the combined box-and-whiskers plot\n",
        "plot_data_combined = []\n",
        "for category in categories:\n",
        "    cat_data = combined_data[combined_data['URCA Category'] == category]\n",
        "    if not cat_data.empty:\n",
        "        box_data = {\n",
        "            'label': category,\n",
        "            'whislo': cat_data['Distance (km)'].min(),\n",
        "            'q1': cat_data['Distance (km)'].quantile(0.25),\n",
        "            'med': cat_data['Distance (km)'].median(),\n",
        "            'q3': cat_data['Distance (km)'].quantile(0.75),\n",
        "            'whishi': cat_data['Distance (km)'].max(),\n",
        "            'mean': cat_data['Distance (km)'].mean(),\n",
        "            'ci95_hi': cat_data['Distance (km)'].mean() + 1.96 * (cat_data['Distance (km)'].std() / np.sqrt(cat_data['Distance (km)'].count())),\n",
        "            'ci95_lo': cat_data['Distance (km)'].mean() - 1.96 * (cat_data['Distance (km)'].std() / np.sqrt(cat_data['Distance (km)'].count())),\n",
        "            'fliers': []\n",
        "        }\n",
        "        plot_data_combined.append(box_data)\n",
        "\n",
        "for idx, data in enumerate(plot_data_combined):\n",
        "    bplot = axes[0].bxp([data], positions=[idx + 1], showmeans=True, meanline=True, showcaps=False, showbox=True,\n",
        "                        patch_artist=True,\n",
        "                        widths=0.5,\n",
        "                        boxprops=dict(facecolor=col[idx // 7], edgecolor='black', linewidth=1),\n",
        "                        medianprops=dict(color='none'), meanprops=dict(color='black'), whiskerprops=dict(color='none'), capprops=dict(color='none'))\n",
        "\n",
        "axes[0].axvline(x=7.5, color='black', linewidth=.5, zorder=0)\n",
        "axes[0].axvline(x=14.5, color='black', linewidth=.5, zorder=0)\n",
        "axes[0].axvline(x=21.5, color='black', linewidth=.5, zorder=0)\n",
        "axes[0].axvline(x=28.5, color='black', linewidth=.5, zorder=0)\n",
        "\n",
        "# Ensure output_df and total_unelectrified_pop are defined\n",
        "output_df = df.copy()\n",
        "total_unelectrified_pop = [output_df[output_df['Country'] == cntry][f'{c+1}_Urban_Unelec_Pop'].sum() + output_df[output_df['Country'] == cntry][f'{c+1}_Rural_Unelec_Pop'].sum() for cntry in country_labels for c in range(30)]\n",
        "\n",
        "# Plot individual country plots\n",
        "for i, cntry in enumerate(country_labels):\n",
        "    country_data = melted_data_list[i]\n",
        "    plot_data = []\n",
        "    for idx, category in enumerate(categories):\n",
        "        if category in country_data['URCA Category'].values:\n",
        "            cat_data = country_data[country_data['URCA Category'] == category]\n",
        "            box_data = {\n",
        "                'label': category,\n",
        "                'whislo': cat_data['Distance (km)'].min(),\n",
        "                'q1': cat_data['Distance (km)'].quantile(0.25),\n",
        "                'med': cat_data['Distance (km)'].median(),\n",
        "                'q3': cat_data['Distance (km)'].quantile(0.75),\n",
        "                'whishi': cat_data['Distance (km)'].max(),\n",
        "                'mean': cat_data['Distance (km)'].mean(),\n",
        "                'ci95_hi': cat_data['Distance (km)'].mean() + 1.96 * (cat_data['Distance (km)'].std() / np.sqrt(cat_data['Distance (km)'].count())),\n",
        "                'ci95_lo': cat_data['Distance (km)'].mean() - 1.96 * (cat_data['Distance (km)'].std() / np.sqrt(cat_data['Distance (km)'].count())),\n",
        "                'fliers': []\n",
        "            }\n",
        "            plot_data.append(box_data)\n",
        "        else:\n",
        "            plot_data.append({\n",
        "                'label': category,\n",
        "                'whislo': np.nan,\n",
        "                'q1': np.nan,\n",
        "                'med': np.nan,\n",
        "                'q3': np.nan,\n",
        "                'whishi': np.nan,\n",
        "                'mean': np.nan,\n",
        "                'ci95_hi': np.nan,\n",
        "                'ci95_lo': np.nan,\n",
        "                'fliers': []\n",
        "            })\n",
        "\n",
        "    axes[i + 1].text(0.01, 0.98, cntry, transform=axes[i + 1].transAxes, fontsize=14, fontweight='bold', va='top', ha='left', fontname='sans-serif')\n",
        "    axes[i + 1].set_ylim(0, custom_y_limits[i + 1])\n",
        "    axes[i + 1].axhline(y=10, color='black', linestyle='-', linewidth=1)  # Add horizontal line at 10 km\n",
        "    axes[i + 1].text(1.8, 18, '10 km', fontsize=10, ha='right', va='center', color='black')  # Label the 10 km line\n",
        "\n",
        "    axes[i + 1].axvline(x=7.5, color='black', linewidth=.5, zorder=0)\n",
        "    axes[i + 1].axvline(x=14.5, color='black', linewidth=.5, zorder=0)\n",
        "    axes[i + 1].axvline(x=21.5, color='black', linewidth=.5, zorder=0)\n",
        "    axes[i + 1].axvline(x=28.5, color='black', linewidth=.5, zorder=0)\n",
        "\n",
        "    # Create secondary y-axis for cumulative percentage line\n",
        "    secax_country = axes[i + 1].twinx()\n",
        "    country_cumulative_percent = graph_4_b[country_labels.index(cntry), :]\n",
        "    secax_country.fill_between(range(1, 31), country_cumulative_percent, color='gray', alpha=0.2, zorder=-1)\n",
        "    secax_country.set_ylim(0, 1)\n",
        "    secax_country.set_yticks([0, 0.25, 0.5, 0.75, 1])\n",
        "    secax_country.set_yticklabels(['0%', '25%', '50%', '75%', '100%'], fontname='sans-serif', fontsize=12)\n",
        "\n",
        "    for idx, data in enumerate(plot_data):\n",
        "        bplot = axes[i + 1].bxp([data], positions=[idx + 1], showmeans=True, meanline=True, showcaps=False, showbox=True,\n",
        "                                patch_artist=True,\n",
        "                                widths=0.5,\n",
        "                                boxprops=dict(facecolor=col[idx // 7], edgecolor='black', linewidth=1),\n",
        "                                medianprops=dict(color='none'), meanprops=dict(color='black'), whiskerprops=dict(color='none'), capprops=dict(color='none'))\n",
        "\n",
        "# Clear all xticks and xticklabels\n",
        "for ax in axes:\n",
        "    ax.set_xticks([])\n",
        "    ax.set_xticklabels([])\n",
        "\n",
        "# Manually add the x-axis label, ticks, and tick labels above the first subplot\n",
        "axes[0].xaxis.set_ticks_position('top')\n",
        "axes[0].xaxis.set_label_position('top')\n",
        "axes[0].set_xticks(range(1, len(categories) + 1))\n",
        "axes[0].set_xticklabels(categories, fontname='sans-serif', fontsize=12)\n",
        "fig.text(0.5, 0.96, 'URCA Category', ha='center', fontname='sans-serif', fontsize=12)\n",
        "for idx, category in enumerate(categories):\n",
        "    axes[0].axvline(x=idx + 1, color='black', linewidth=0.5, ymin=1.02, ymax=1.04)  # Add tick marks\n",
        "\n",
        "# Set the x-axis labels only for the last plot\n",
        "axes[-1].xaxis.set_ticks_position('bottom')\n",
        "axes[-1].xaxis.set_label_position('bottom')\n",
        "axes[-1].set_xticks(range(1, len(categories) + 1))\n",
        "axes[-1].set_xticklabels(x_axis_labels, fontname='sans-serif', fontsize=12, rotation=90, ha='center')\n",
        "\n",
        "# Add center-justified text below lower axis labels\n",
        "plt.text(4, -1.6, 'Within urban core', ha='center', va='center', fontsize=14)\n",
        "plt.text(11, -1.6, '<1 hour to city', ha='center', va='center', fontsize=14)\n",
        "plt.text(18, -1.6, '1-2 hours from city', ha='center', va='center', fontsize=14)\n",
        "plt.text(25, -1.6, '2-3 hours from city', ha='center', va='center', fontsize=14)\n",
        "\n",
        "# Add a single y-axis label for both left and right\n",
        "fig.text(0.04, 0.5, 'Distance from nearest electrified cell (km)', va='center', rotation='vertical', fontname='sans-serif', fontsize=12)\n",
        "fig.text(0.96, 0.5, 'Cumulative percent of unelectrified population', va='center', rotation='vertical', fontname='sans-serif', fontsize=12)\n",
        "\n",
        "# Save the plot\n",
        "plt.tight_layout(rect=[0.04, 0.04, 0.96, 0.96])  # Adjust rect to make room for y-axis labels\n",
        "plt.savefig(os.path.join(path, 'Graphics/physical_distribution.png'), bbox_inches='tight', dpi=500)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Gjymq62_WcnR"
      },
      "id": "Gjymq62_WcnR",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "N3th47MALZTd",
      "metadata": {
        "id": "N3th47MALZTd"
      },
      "source": [
        "# **In-text citations**"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## External citations"
      ],
      "metadata": {
        "id": "If_XI3Mv_n-a"
      },
      "id": "If_XI3Mv_n-a"
    },
    {
      "cell_type": "markdown",
      "id": "US2nkK13L-vX",
      "metadata": {
        "id": "US2nkK13L-vX"
      },
      "source": [
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4SbmwZ8YMArA",
      "metadata": {
        "id": "4SbmwZ8YMArA"
      },
      "source": [
        "https://trackingsdg7.esmap.org/data/files/download-documents/sdg7-report2023-ch1._access_to_electricity.pdf"
      ]
    },
    {
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      "id": "FPKSznKALayi",
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          "height": 238
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        "outputId": "21223391-a0f5-4545-b583-59fd52986af3"
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "     Country         Percentage  Cumulative Percentage\n",
              "0    Nigeria 15.167548500881834     15.167548500881834\n",
              "1        DRC 13.403880070546737      28.57142857142857\n",
              "2   Ethiopia  9.700176366843033       38.2716049382716\n",
              "3   Tanzania  6.349206349206349      44.62081128747795\n",
              "4     Uganda  4.409171075837742      49.02998236331569\n",
              "14     Kenya 2.1164021164021163       51.1463844797178"
            ],
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              "      <td>Nigeria</td>\n",
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              "      <td>15.167548500881834</td>\n",
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              "      <td>DRC</td>\n",
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              "      <td>28.57142857142857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Ethiopia</td>\n",
              "      <td>9.700176366843033</td>\n",
              "      <td>38.2716049382716</td>\n",
              "    </tr>\n",
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              "      <th>3</th>\n",
              "      <td>Tanzania</td>\n",
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              "      <td>Uganda</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(selected_df[['Country', 'Percentage', 'Cumulative Percentage']])\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Nigeria\",\n          \"DRC\",\n          \"Kenya\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Percentage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.139109090520018,\n        \"min\": 2.1164021164021163,\n        \"max\": 15.167548500881834,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          15.167548500881834,\n          13.403880070546737,\n          2.1164021164021163\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cumulative Percentage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.772281886699616,\n        \"min\": 15.167548500881834,\n        \"max\": 51.1463844797178,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          15.167548500881834,\n          28.57142857142857,\n          51.1463844797178\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Unelectrified population data\n",
        "unelec_pop_WB_2021 = [86, 76, 55, 36, 25, 22, 21, 19, 18, 18, 17, 17, 15, 15, 12, 12, 12, 11, 10, 10, 166]\n",
        "countries_WB_2021 = ['Nigeria', 'DRC', 'Ethiopia', 'Tanzania', 'Uganda', 'Mozambique', 'Niger', 'Madagascar', 'Burkina Faso', 'Angola', 'Sudan', 'Malawi', 'Chad', 'Myanmar', 'Kenya', 'North Korea', 'Pakistan', 'Burundi', 'Zambia', 'Mali', 'Rest of world']\n",
        "selected_countries = ['DRC', 'Ethiopia', 'Tanzania', 'Nigeria', 'Kenya', 'Uganda']\n",
        "\n",
        "# Overall unelectrified population in Africa, 2021\n",
        "Africa_unelec_2021 = 567 # IEA, IRENA, UNSD, World Bank, WHO, “Tracking SDG 7: The Energy Progress Report” (The World Bank, 2021).\n",
        "\n",
        "# Create a DataFrame\n",
        "data = {'Country': countries_WB_2021, 'Unelectrified Population (millions)': unelec_pop_WB_2021}\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "# Calculate total unelectrified population\n",
        "total_unelec_pop = Africa_unelec_2021\n",
        "\n",
        "# Calculate percentage of each country\n",
        "df['Percentage'] = (df['Unelectrified Population (millions)'] / total_unelec_pop) * 100\n",
        "\n",
        "# Extract the percentages of the selected countries\n",
        "selected_df = df[df['Country'].isin(selected_countries)]\n",
        "\n",
        "# Calculate cumulative percentage of the selected countries\n",
        "selected_df = selected_df.sort_values(by='Percentage', ascending=False)\n",
        "selected_df['Cumulative Percentage'] = selected_df['Percentage'].cumsum()\n",
        "\n",
        "# Display the results\n",
        "display(selected_df[['Country', 'Percentage', 'Cumulative Percentage']])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## URCA calculations"
      ],
      "metadata": {
        "id": "gqy-buyw_ske"
      },
      "id": "gqy-buyw_ske"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "qj6gUQuEPxSP",
      "metadata": {
        "id": "qj6gUQuEPxSP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c94e804c-79a2-44c6-f496-27ec71daa445"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "URCA 1-7 population as a percent of total population: 26.25%\n",
            "URCA 1-7 unelectrified population as a percent of total unelectrified population: 6.03%\n",
            "\n",
            "URCA 8-14 population as a percent of total population: 47.45%\n",
            "URCA 8-14 unelectrified population as a percent of total unelectrified population: 56.60%\n",
            "\n",
            "URCA 15-21 population as a percent of total population: 16.69%\n",
            "URCA 15-21 unelectrified population as a percent of total unelectrified population: 23.22%\n",
            "\n",
            "URCA 22-28 population as a percent of total population: 4.12%\n",
            "URCA 22-28 unelectrified population as a percent of total unelectrified population: 6.05%\n",
            "\n",
            "URCA 29-30 population as a percent of total population: 5.49%\n",
            "URCA 29-30 unelectrified population as a percent of total unelectrified population: 8.10%\n",
            "\n"
          ]
        }
      ],
      "source": [
        "df_path = os.path.join(path, 'Outputs/rural_urban_elec_unelec_URCA_output.csv')\n",
        "\n",
        "# Read in the data\n",
        "df = pd.read_csv(df_path)\n",
        "\n",
        "# Calculate total population and unelectrified population\n",
        "total_population = df[[f'{i}_Pop' for i in range(1, 31)]].sum().sum()\n",
        "total_unelectrified = df[[f'{i}_Unelec_Pop' for i in range(1, 31)]].sum().sum()\n",
        "\n",
        "# Function to calculate percentages for a given range of URCA categories\n",
        "def calculate_percentages(start, end):\n",
        "    population = df[[f'{i}_Pop' for i in range(start, end + 1)]].sum().sum()\n",
        "    unelectrified = df[[f'{i}_Unelec_Pop' for i in range(start, end + 1)]].sum().sum()\n",
        "    percent_population = (population / total_population) * 100\n",
        "    percent_unelectrified = (unelectrified / total_unelectrified) * 100\n",
        "    return percent_population, percent_unelectrified\n",
        "\n",
        "# Calculate and print percentages for each URCA range\n",
        "urca_ranges = [(1, 7), (8, 14), (15, 21), (22, 28), (29, 30)]\n",
        "for start, end in urca_ranges:\n",
        "    percent_population, percent_unelectrified = calculate_percentages(start, end)\n",
        "    print(f\"URCA {start}-{end} population as a percent of total population: {percent_population:.2f}%\")\n",
        "    print(f\"URCA {start}-{end} unelectrified population as a percent of total unelectrified population: {percent_unelectrified:.2f}%\\n\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_path = os.path.join(path, 'Outputs/rural_urban_elec_unelec_URCA_output.csv')\n",
        "\n",
        "# Read in the data\n",
        "df = pd.read_csv(df_path)\n",
        "\n",
        "# Filter data for specific countries\n",
        "uganda_data = df[df['Country'] == 'Uganda']\n",
        "drc_data = df[df['Country'] == 'DRC']\n",
        "\n",
        "# Calculate total unelectrified population for each country\n",
        "uganda_total_unelectrified = uganda_data[[f'{i}_Unelec_Pop' for i in range(1, 31)]].sum().sum()\n",
        "drc_total_unelectrified = drc_data[[f'{i}_Unelec_Pop' for i in range(1, 31)]].sum().sum()\n",
        "\n",
        "# Calculate unelectrified population for URCA 1-14 for Uganda\n",
        "uganda_urca_1_14_unelectrified = uganda_data[[f'{i}_Unelec_Pop' for i in range(1, 15)]].sum().sum()\n",
        "uganda_urca_1_14_percent = (uganda_urca_1_14_unelectrified / uganda_total_unelectrified) * 100\n",
        "\n",
        "# Calculate unelectrified population for URCA 15-30 for DRC\n",
        "drc_urca_15_30_unelectrified = drc_data[[f'{i}_Unelec_Pop' for i in range(15, 31)]].sum().sum()\n",
        "drc_urca_15_30_percent = (drc_urca_15_30_unelectrified / drc_total_unelectrified) * 100\n",
        "\n",
        "# Print the results\n",
        "print(f\"Population of unelectrified living in URCA 1-14, Uganda: {uganda_urca_1_14_percent:.2f}%\")\n",
        "print(f\"Population of unelectrified living in URCA 15-30, DRC: {drc_urca_15_30_percent:.2f}%\")"
      ],
      "metadata": {
        "id": "w60SaY02yenG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "534ca843-9b4c-4ccb-b616-16657b74fd45"
      },
      "id": "w60SaY02yenG",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Population of unelectrified living in URCA 1-14, Uganda: 78.76%\n",
            "Population of unelectrified living in URCA 15-30, DRC: 47.13%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_path = os.path.join(path, 'Outputs/rural_urban_elec_unelec_URCA_output.csv')\n",
        "\n",
        "# Read in the data\n",
        "df = pd.read_csv(df_path)\n",
        "\n",
        "# Filter data for specific countries\n",
        "nigeria_data = df[df['Country'] == 'Nigeria']\n",
        "ethiopia_data = df[df['Country'] == 'Ethiopia']\n",
        "\n",
        "# Calculate total population for each country\n",
        "nigeria_total_population = nigeria_data[[f'{i}_Pop' for i in range(1, 31)]].sum().sum()\n",
        "ethiopia_total_population = ethiopia_data[[f'{i}_Pop' for i in range(1, 31)]].sum().sum()\n",
        "\n",
        "# Calculate urban population for each country\n",
        "nigeria_urban_population = nigeria_data[[f'{i}_Urban_Pop' for i in range(1, 31)]].sum().sum()\n",
        "ethiopia_urban_population = ethiopia_data[[f'{i}_Urban_Pop' for i in range(1, 31)]].sum().sum()\n",
        "\n",
        "# Calculate urbanization percentage for each country\n",
        "nigeria_urbanization_percent = (nigeria_urban_population / nigeria_total_population) * 100\n",
        "ethiopia_urbanization_percent = (ethiopia_urban_population / ethiopia_total_population) * 100\n",
        "\n",
        "# Calculate total unelectrified population for Nigeria\n",
        "nigeria_total_unelectrified = nigeria_data[[f'{i}_Unelec_Pop' for i in range(1, 31)]].sum().sum()\n",
        "\n",
        "# Calculate unelectrified population for URCA 12 for Nigeria\n",
        "nigeria_urca_12_unelectrified = nigeria_data['12_Unelec_Pop'].sum()\n",
        "nigeria_urca_12_percent = (nigeria_urca_12_unelectrified / nigeria_total_unelectrified) * 100\n",
        "\n",
        "# Calculate unelectrified population for URCA 15-30 for Nigeria\n",
        "nigeria_urca_15_30_unelectrified = nigeria_data[[f'{i}_Unelec_Pop' for i in range(15, 31)]].sum().sum()\n",
        "nigeria_urca_15_30_percent = (nigeria_urca_15_30_unelectrified / nigeria_total_unelectrified) * 100\n",
        "\n",
        "# Print the results\n",
        "print(f\"Nigeria urbanization rate: {nigeria_urbanization_percent:.2f}%\")\n",
        "print(f\"Ethiopia urbanization rate: {ethiopia_urbanization_percent:.2f}%\")\n",
        "print(f\"Nigeria, URCA 12 as percent of total unelectrified: {nigeria_urca_12_percent:.2f}%\")\n",
        "print(f\"Nigeria, total unelectrified population: {nigeria_total_unelectrified / 1e6:.2f} million\")\n",
        "print(f\"Nigeria, URCA 15-30 as percent of total unelectrified population: {nigeria_urca_15_30_percent:.2f}%\")"
      ],
      "metadata": {
        "id": "kFwiYobI_Kkx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "706f239f-3376-45ad-f90f-3b9af097ed82"
      },
      "id": "kFwiYobI_Kkx",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Nigeria urbanization rate: 42.83%\n",
            "Ethiopia urbanization rate: 31.48%\n",
            "Nigeria, URCA 12 as percent of total unelectrified: 21.89%\n",
            "Nigeria, total unelectrified population: 116.07 million\n",
            "Nigeria, URCA 15-30 as percent of total unelectrified population: 10.22%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_path = os.path.join(path, 'Outputs/rural_urban_elec_unelec_URCA_output.csv')\n",
        "\n",
        "# Read in the data\n",
        "df = pd.read_csv(df_path)\n",
        "\n",
        "# Calculate urbanization rate for Nigeria for URCA categories 1-10\n",
        "calculate_figure(df, 'urbanization_rate', list(range(1, 11)), country='Nigeria')\n",
        "\n",
        "# Calculate electrification rate for all countries for URCA categories 15-30\n",
        "calculate_figure(df, 'electrification_rate', list(range(15, 31)), country='all')\n",
        "\n",
        "# Calculate unelectrified population percent for DRC for URCA categories 22-30\n",
        "calculate_figure(df, 'unelectrified_population_percent', list(range(22, 31)), country='DRC')\n",
        "\n",
        "# Calculate unelectrified population percent for Nigeria for URCA categories 22-30\n",
        "calculate_figure(df, 'unelectrified_population_percent', list(range(22, 31)), country='Nigeria')\n",
        "\n",
        "# Calculate unelectrified population percent for all countries for URCA categories 7-14\n",
        "calculate_figure(df, 'unelectrified_population_percent', list(range(7, 15)), country='all')\n",
        "\n",
        "# Calculate unelectrified population percent for all countries for URCA category 12\n",
        "calculate_figure(df, 'unelectrified_population_percent', 12, country='all')\n"
      ],
      "metadata": {
        "id": "BSzp7WnLVO98",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ff128d0f-a26c-4ee0-f998-a84c7172ef47"
      },
      "id": "BSzp7WnLVO98",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The urbanization rate for Nigeria in URCA 1-10 is 62.41%.\n",
            "The electrification rate for all countries in URCA 15-30 is 5.49%.\n",
            "The unelectrified population percent for DRC in URCA 22-30 is 23.60%.\n",
            "The unelectrified population percent for Nigeria in URCA 22-30 is 1.87%.\n",
            "The unelectrified population percent for all countries in URCA 7-14 is 58.35%.\n",
            "The unelectrified population percent for all countries in URCA 12 is 19.79%.\n",
            "The unelectrified population percent for all countries in URCA 12-19 is 27.51%.\n",
            "The total population for all countries in URCA 12-19 is 214153145.00.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate unelectrified population percent for all countries for URCA category 12\n",
        "calculate_figure(df, 'unelectrified_population', [12, 19], country='all')\n",
        "calculate_figure(df, 'unelectrified_population',  list(range(1, 31)), country='all')\n",
        "\n",
        "# Calculate unelectrified population percent for all countries for URCA category 12\n",
        "calculate_figure(df, 'total_population', [12, 19], country='all')\n",
        "calculate_figure(df, 'total_population',  list(range(1, 31)), country='all')\n",
        "\n",
        "print(214153145.00/1005011787.00)\n",
        "print(183891285.00/668516473.00)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5tUh5pXG4WnJ",
        "outputId": "73baba16-df66-43ff-9688-c45f2d9abd71"
      },
      "id": "5tUh5pXG4WnJ",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The unelectrified population for all countries in URCA 12-19 is 183891285.00.\n",
            "The unelectrified population for all countries in URCA 1-30 is 668516473.00.\n",
            "The total population for all countries in URCA 12-19 is 214153145.00.\n",
            "The total population for all countries in URCA 1-30 is 1005011787.00.\n",
            "0.2130852073280211\n",
            "0.275073677952585\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate electrification rate for all countries in URCA categories 1-3\n",
        "calculate_figure(df, 'electrification_rate', list(range(1, 4)), country='all')\n",
        "\n",
        "# Calculate electrification rate for all countries in URCA category 6\n",
        "calculate_figure(df, 'electrification_rate', 6, country='all')\n",
        "\n",
        "# Calculate electrification rate for all countries in URCA category 7\n",
        "calculate_figure(df, 'electrification_rate', 7, country='all')"
      ],
      "metadata": {
        "id": "Rk5iCfrdr97e"
      },
      "id": "Rk5iCfrdr97e",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate unelectrified population percent for Nigeria in URCA categories 8-13\n",
        "calculate_figure(df, 'unelectrified_population_percent', list(range(8,13)), country='Nigeria')\n",
        "\n",
        "# Calculate unelectrified population percent for Nigeria in URCA categories 14-30\n",
        "calculate_figure(df, 'unelectrified_population_percent', list(range(14,31)), country='Nigeria')\n",
        "\n",
        "# Calculate unelectrified population percent for Nigeria in URCA categories 1-3\n",
        "calculate_figure(df, 'unelectrified_population', list(range(1,31)), country='Nigeria')"
      ],
      "metadata": {
        "id": "SDnzm645skW-"
      },
      "id": "SDnzm645skW-",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate unelectrified population percent for Kenya in URCA categories 1-14\n",
        "calculate_figure(df, 'unelectrified_population_percent', list(range(1,15)), country='Kenya')\n",
        "\n",
        "# Calculate unelectrified population percent for Nigeria in URCA categories 14-30\n",
        "calculate_figure(df, 'unelectrified_population_percent', list(range(14,31)), country='Nigeria')\n",
        "\n",
        "# Calculate unelectrified population percent for Nigeria in URCA categories 1-3\n",
        "calculate_figure(df, 'unelectrified_population', list(range(1,31)), country='Nigeria')"
      ],
      "metadata": {
        "id": "0NCZvo_Ztc01"
      },
      "id": "0NCZvo_Ztc01",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate unelectrified population percent for all countries in URCA categories 5, 12, 19, 26\n",
        "calculate_figure(df, 'unelectrified_population_percent', list([5, 12, 19, 26]), country='all')"
      ],
      "metadata": {
        "id": "NEHYbd9TATae"
      },
      "id": "NEHYbd9TATae",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate cumulative unelectrified population in Nigeria\n",
        "display(calculate_figure(df, 'cumulative_unelectrified_percent', list(range(1, 15)), country='Nigeria'))"
      ],
      "metadata": {
        "id": "5Rzr3912DmqR"
      },
      "id": "5Rzr3912DmqR",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate cumulative unelectrified population in Tanzania and DRC\n",
        "display(calculate_figure(df, 'cumulative_unelectrified_percent', list(range(1, 15)), country='Tanzania'))\n",
        "display(calculate_figure(df, 'cumulative_unelectrified_percent', list(range(1, 15)), country='DRC'))"
      ],
      "metadata": {
        "id": "Wy13xx3Vh6z-"
      },
      "id": "Wy13xx3Vh6z-",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "display(calculate_figure(df, 'unelectrified_population_percent', list(range(1, 15)), country='all'))"
      ],
      "metadata": {
        "id": "YeVPrmv1_3WH",
        "outputId": "5dcf9850-9faf-4b68-cffa-6d8531ff4553",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "id": "YeVPrmv1_3WH",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The unelectrified population percent for all countries in URCA 1-14 is 62.63%.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "None"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_path = os.path.join(path, 'Outputs/all_agglomerations_unelectrified_population.csv')\n",
        "\n",
        "# Read in the data\n",
        "df = pd.read_csv(df_path)\n",
        "\n",
        "# Define population size categories\n",
        "population_bins = [20000, 50000, 100000, 250000, 500000, 1000000, 5000000, float('inf')]\n",
        "population_labels = [\n",
        "    '20,000-50,000',\n",
        "    '50,000-100,000',\n",
        "    '100,000-250,000',\n",
        "    '250,000-500,000',\n",
        "    '500,000-1 million',\n",
        "    '1-5 million',\n",
        "    '5 million +'\n",
        "]\n",
        "\n",
        "# Create a new column for population size categories\n",
        "df['Population_Category'] = pd.cut(df['Pop2015'], bins=population_bins, labels=population_labels)\n",
        "\n",
        "# Initialize the list to store the results\n",
        "results_list = []\n",
        "\n",
        "# Define the radii in kilometers\n",
        "radii_km = [5, 10, 20, 30, 50]\n",
        "\n",
        "# Calculate the statistics for each population category and radius\n",
        "for population_category in population_labels:\n",
        "    for radius in radii_km:\n",
        "        filtered_df = df[(df['Population_Category'] == population_category) & (df['Radius_Kilometers'] == radius)]\n",
        "        city_count = len(filtered_df)\n",
        "        if city_count > 0:\n",
        "            average_pop2015 = filtered_df['Pop2015'].mean()\n",
        "            weighted_average_electrification = (filtered_df['Electrification_Rate'] * filtered_df['Pop2015']).sum() / filtered_df['Pop2015'].sum()\n",
        "        else:\n",
        "            average_pop2015 = 0\n",
        "            weighted_average_electrification = 0\n",
        "\n",
        "        results_list.append({\n",
        "            'Population_Category': population_category,\n",
        "            'Radius_Kilometers': radius,\n",
        "            'City_Count': city_count,\n",
        "            'Average_Pop2015': average_pop2015,\n",
        "            'Weighted_Average_Electrification': weighted_average_electrification\n",
        "        })\n",
        "\n",
        "# Convert the results list to a DataFrame\n",
        "results_table = pd.DataFrame(results_list)\n",
        "\n",
        "# Print the results\n",
        "display(results_table)"
      ],
      "metadata": {
        "id": "1Rb5HCb5ALbf"
      },
      "id": "1Rb5HCb5ALbf",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_path = os.path.join(path, 'Outputs/rural_urban_elec_unelec_URCA_output.csv')\n",
        "\n",
        "# Read in the data\n",
        "df = pd.read_csv(df_path)\n",
        "\n",
        "# Sum the population and electrification data across all countries\n",
        "summary_data = df.sum(numeric_only=True)\n",
        "\n",
        "# Create a summary DataFrame\n",
        "summary_df = pd.DataFrame(summary_data).transpose()\n",
        "\n",
        "# Add a 'Country' column with a value 'Total'\n",
        "summary_df['Country'] = 'Total'\n",
        "\n",
        "# Reorder columns to have 'Country' first\n",
        "cols = summary_df.columns.tolist()\n",
        "cols = cols[-1:] + cols[:-1]\n",
        "summary_df = summary_df[cols]\n",
        "\n",
        "# Calculate electrification rate per URCA category\n",
        "electrification_rates = {}\n",
        "for i in range(1, 31):  # Assuming URCA categories are from 1 to 30\n",
        "    total_pop = summary_df[f'{i}_Pop'].values[0]\n",
        "    elec_pop = summary_df[f'{i}_Elec_Pop'].values[0]\n",
        "    if total_pop > 0:\n",
        "        electrification_rates[f'{i}'] = elec_pop / total_pop\n",
        "    else:\n",
        "        electrification_rates[f'{i}'] = 0\n",
        "\n",
        "# Create a DataFrame for electrification rates\n",
        "electrification_df = pd.DataFrame(list(electrification_rates.items()), columns=['URCA Category', 'Electrification Rate'])\n",
        "\n",
        "# Print the summary DataFrame and electrification rates\n",
        "print(\"Summary DataFrame:\")\n",
        "print(summary_df)\n",
        "\n",
        "print(\"\\nElectrification Rates per URCA Category:\")\n",
        "display(electrification_df)"
      ],
      "metadata": {
        "id": "Tpr0Ba14BxTc"
      },
      "id": "Tpr0Ba14BxTc",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Distance calculations"
      ],
      "metadata": {
        "id": "HMdSpOld_iGY"
      },
      "id": "HMdSpOld_iGY"
    },
    {
      "cell_type": "code",
      "source": [
        "def calculate_population_within_distance(dists_path, country, distance):\n",
        "    # Initialize a variable to hold the total population within the specified distance\n",
        "    total_population_within_distance = 0\n",
        "    total_population_all = 0\n",
        "\n",
        "    # Check if 'country' is a list or a string\n",
        "    if isinstance(country, str):\n",
        "        country = [country]  # Convert to a list with one element if it's a string\n",
        "\n",
        "    # Loop through each country in the list\n",
        "    for cntry in country:\n",
        "        # Load the distances and populations data for the country\n",
        "        df_distances = pd.read_csv(os.path.join(dists_path, f'{cntry}_Dists.csv'))\n",
        "        df_populations = pd.read_csv(os.path.join(dists_path, f'{cntry}_Populations.csv'))\n",
        "\n",
        "        # Drop the 'Country' column if it exists\n",
        "        if 'Country' in df_distances.columns:\n",
        "            df_distances = df_distances.drop(columns='Country')\n",
        "        if 'Country' in df_populations.columns:\n",
        "            df_populations = df_populations.drop(columns='Country')\n",
        "\n",
        "        # Find all positions where the distance is less than or equal to the specified distance\n",
        "        mask = df_distances <= distance\n",
        "\n",
        "        # Sum the corresponding populations for those distances\n",
        "        population_within_distance = df_populations[mask].sum().sum()\n",
        "\n",
        "        # Calculate the total population\n",
        "        total_population = df_populations.sum().sum()\n",
        "\n",
        "        # Update the total population across all countries\n",
        "        total_population_within_distance += population_within_distance\n",
        "        total_population_all += total_population\n",
        "\n",
        "        # Print the result for the individual country\n",
        "        percentage_within_distance = (population_within_distance / total_population) * 100\n",
        "        print(f\"{cntry}: {percentage_within_distance:.2f}% of the population is within {distance} km.\")\n",
        "\n",
        "    # Calculate the percentage for all countries combined\n",
        "    overall_percentage_within_distance = (total_population_within_distance / total_population_all) * 100 if total_population_all > 0 else np.nan\n",
        "\n",
        "    if len(country) > 1:  # If more than one country was processed\n",
        "        print(f\"Overall: {overall_percentage_within_distance:.2f}% of the population is within {distance} km across all countries.\")\n",
        "\n",
        "    return overall_percentage_within_distance if len(country) > 1 else percentage_within_distance\n"
      ],
      "metadata": {
        "id": "wnWA4kk_5-6s"
      },
      "id": "wnWA4kk_5-6s",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dists_path = os.path.join(path, 'Dists/')\n",
        "print(calculate_population_within_distance(dists_path, 'Uganda', 10))\n",
        "print(calculate_population_within_distance(dists_path, 'Tanzania', 10))\n",
        "print(calculate_population_within_distance(dists_path, 'Nigeria', 10))\n",
        "print(calculate_population_within_distance(dists_path, 'Kenya', 10))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VtDlWFuZ6IDw",
        "outputId": "e65ef7f6-20e6-4cd6-944a-844fea867b50"
      },
      "id": "VtDlWFuZ6IDw",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Uganda: 60.36% of the population is within 10 km.\n",
            "60.3566450125521\n",
            "Tanzania: 47.56% of the population is within 10 km.\n",
            "47.556695240445315\n",
            "Nigeria: 59.75% of the population is within 10 km.\n",
            "59.74532817103623\n",
            "Kenya: 75.21% of the population is within 10 km.\n",
            "75.20663044402905\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(calculate_population_within_distance(dists_path, 'DRC', 10))\n",
        "print(calculate_population_within_distance(dists_path, 'DRC', 50))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Eqn3xldV9ySQ",
        "outputId": "981f6d80-bf9e-4bdc-c9d5-12e2a6c3fca2"
      },
      "id": "Eqn3xldV9ySQ",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DRC: 27.71% of the population is within 10 km.\n",
            "27.707501917970767\n",
            "DRC: 62.50% of the population is within 50 km.\n",
            "62.49649274410326\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(calculate_population_within_distance(dists_path, countries, 5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RGr5vdnU-R6D",
        "outputId": "8aaa7ea0-341e-43e5-f7ad-26aa2628db3f"
      },
      "id": "RGr5vdnU-R6D",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Angola: 17.82% of the population is within 5 km.\n",
            "Benin: 41.16% of the population is within 5 km.\n",
            "Botswana: 36.72% of the population is within 5 km.\n",
            "Burkina Faso: 16.50% of the population is within 5 km.\n",
            "Burundi: 31.92% of the population is within 5 km.\n",
            "Cameroon: 25.41% of the population is within 5 km.\n",
            "Central African Republic: 17.20% of the population is within 5 km.\n",
            "Chad: 15.71% of the population is within 5 km.\n",
            "DRC: 18.96% of the population is within 5 km.\n",
            "ROC: 21.91% of the population is within 5 km.\n",
            "CDI: 51.38% of the population is within 5 km.\n",
            "Equatorial Guinea: 44.21% of the population is within 5 km.\n",
            "Eritrea: 19.03% of the population is within 5 km.\n",
            "Swaziland: 61.50% of the population is within 5 km.\n",
            "Ethiopia: 18.34% of the population is within 5 km.\n",
            "Gabon: 24.36% of the population is within 5 km.\n",
            "Gambia: 61.12% of the population is within 5 km.\n",
            "Ghana: 67.30% of the population is within 5 km.\n",
            "Guinea: 21.78% of the population is within 5 km.\n",
            "Guinea-Bissau: 34.96% of the population is within 5 km.\n",
            "Kenya: 53.96% of the population is within 5 km.\n",
            "Lesotho: 36.93% of the population is within 5 km.\n",
            "Liberia: 26.73% of the population is within 5 km.\n",
            "Madagascar: 15.63% of the population is within 5 km.\n",
            "Malawi: 44.84% of the population is within 5 km.\n",
            "Mali: 12.49% of the population is within 5 km.\n",
            "Mauritania: 23.76% of the population is within 5 km.\n",
            "Mozambique: 26.70% of the population is within 5 km.\n",
            "Namibia: 44.29% of the population is within 5 km.\n",
            "Niger: 13.60% of the population is within 5 km.\n",
            "Nigeria: 36.50% of the population is within 5 km.\n",
            "Rwanda: 49.97% of the population is within 5 km.\n",
            "Senegal: 41.55% of the population is within 5 km.\n",
            "Sierra Leone: 23.68% of the population is within 5 km.\n",
            "Somalia: 15.22% of the population is within 5 km.\n",
            "South Africa: 60.15% of the population is within 5 km.\n",
            "South Sudan: 12.12% of the population is within 5 km.\n",
            "Sudan: 23.05% of the population is within 5 km.\n",
            "Tanzania: 26.77% of the population is within 5 km.\n",
            "Togo: 45.87% of the population is within 5 km.\n",
            "Uganda: 32.31% of the population is within 5 km.\n",
            "Zambia: 24.95% of the population is within 5 km.\n",
            "Zimbabwe: 27.00% of the population is within 5 km.\n",
            "Overall: 28.71% of the population is within 5 km across all countries.\n",
            "28.70817123856324\n"
          ]
        }
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